code stringlengths 17 6.64M |
|---|
def Convolution(data, num_filter, kernel, stride=None, dilate=None, pad=None, num_group=1, no_bias=False, weight=None, bias=None, name=None, lr_mult=1, reuse=None, **kwargs):
if (reuse is not None):
assert (name is not None)
name = (GetLayerName.get('conv') if (name is None) else name)
stride = ((... |
def Deconvolution(data, num_filter, kernel, stride=None, dilate=None, pad=None, adj=None, target_shape=None, num_group=1, no_bias=False, weight=None, bias=None, name=None, lr_mult=1, reuse=None):
if (reuse is not None):
assert (name is not None)
name = (GetLayerName.get('deconv') if (name is None) els... |
def FullyConnected(data, num_hidden, flatten=True, no_bias=False, weight=None, bias=None, name=None, lr_mult=1, reuse=None):
if (reuse is not None):
assert (name is not None)
name = (GetLayerName.get('fc') if (name is None) else name)
W = (get_variable((name + '_weight'), lr_mult, reuse) if (weigh... |
def Relu(data, name=None):
name = (GetLayerName.get('relu') if (name is None) else name)
x = mx.sym.Activation(data, act_type='relu', name=name)
return x
|
def LeakyRelu(data, slope=0.25, name=None):
name = (GetLayerName.get('leakyRelu') if (name is None) else name)
x = mx.sym.LeakyReLU(data, slope=slope, act_type='leaky', name=name)
return x
|
def Tanh(data, name=None):
name = (GetLayerName.get('tanh') if (name is None) else name)
x = mx.sym.tanh(data, name=name)
return x
|
def Swish(data, name=None):
name = (GetLayerName.get('swish') if (name is None) else name)
x = (data * mx.sym.sigmoid(data))
return x
|
def Pooling(data, kernel, stride=None, pad=None, pool_type='max', global_pool=False, name=None):
name = (GetLayerName.get('pool') if (name is None) else name)
stride = (kernel if (stride is None) else stride)
pad = (((0,) * len(kernel)) if (pad is None) else pad)
x = mx.sym.Pooling(data, kernel=kernel... |
def Dropout(data, p, name=None):
name = (GetLayerName.get('drop') if (name is None) else name)
x = mx.sym.Dropout(data, p=p, name=name)
return x
|
def BatchNorm(data, fix_gamma=False, momentum=0.9, eps=1e-05, use_global_stats=False, gamma=None, beta=None, moving_mean=None, moving_var=None, name=None, lr_mult=1, reuse=None):
if (reuse is not None):
assert (name is not None)
name = (GetLayerName.get('bn') if (name is None) else name)
gamma = (... |
def InstanceNorm(data, eps=1e-05, gamma=None, beta=None, name=None, lr_mult=1, reuse=None):
if (reuse is not None):
assert (name is not None)
name = (GetLayerName.get('in') if (name is None) else name)
gamma = (get_variable((name + '_gamma'), lr_mult, reuse) if (gamma is None) else gamma)
beta... |
def Flatten(data, name=None):
name = (GetLayerName.get('flatten') if (name is None) else name)
x = mx.sym.flatten(data, name=name)
return x
|
def ConvRelu(*args, **kwargs):
x = Conv(*args, **kwargs)
x = Relu(x, (x.name + '_relu'))
return x
|
def BNRelu(*args, **kwargs):
x = BN(*args, **kwargs)
x = Relu(x, (x.name + '_relu'))
return x
|
def FCRelu(*args, **kwargs):
x = FC(*args, **kwargs)
x = Relu(x, (x.name + '_relu'))
return x
|
def ConvBNRelu(*args, **kwargs):
x = Conv(*args, **kwargs)
x = BN(x, name=(x.name + '_bn'), lr_mult=kwargs.get('lr_mult', 1), reuse=kwargs.get('reuse', None))
x = Relu(x, (x.name + '_relu'))
return x
|
def get_variable(name, lr_mult=1, reuse=None):
if (reuse is None):
return mx.sym.Variable(name, lr_mult=lr_mult)
return reuse.get_internals()[name]
|
class GetLayerName(object):
_name_count = {}
@classmethod
def get(cls, name_prefix):
cnt = cls._name_count.get(name_prefix, 0)
cls._name_count[name_prefix] = (cnt + 1)
return (name_prefix + str(cnt))
|
def padding_helper(in_size, kernel_size, stride, pad_type='same'):
pad_type = pad_type.lower()
if (pad_type == 'same'):
out_size = ((in_size // stride) + int(((in_size % stride) > 0)))
pad_size = max(((((out_size - 1) * stride) + kernel_size) - in_size), 0)
return ((pad_size // 2), (pa... |
class OpConstant(mx.operator.CustomOp):
def __init__(self, val):
self.val = val
def forward(self, is_train, req, in_data, out_data, aux):
self.assign(out_data[0], req[0], self.val)
def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
pass
|
@mx.operator.register('Constant')
class OpConstantProp(mx.operator.CustomOpProp):
def __init__(self, val_str, shape_str, type_str='float32'):
super(OpConstantProp, self).__init__(need_top_grad=False)
val = [float(x) for x in val_str.split(',')]
shape = [int(x) for x in shape_str.split(','... |
def CustomConstantEncoder(value, dtype='float32'):
if (not isinstance(value, np.ndarray)):
if (not isinstance(value, (list, tuple))):
value = [value]
value = np.array(value, dtype=dtype)
return (','.join([str(x) for x in value.ravel()]), ','.join([str(x) for x in value.shape]))
|
def Constant(value, dtype='float32'):
assert isinstance(dtype, str), dtype
(val, shape) = CustomConstantEncoder(value, dtype)
return mx.sym.Custom(val_str=val, shape_str=shape, type_str=dtype, op_type='Constant')
|
class BilinearScale(mx.operator.CustomOp):
def __init__(self, scale):
self.scale = scale
def forward(self, is_train, req, in_data, out_data, aux):
x = in_data[0]
(h, w) = x.shape[2:]
new_h = (int(((h - 1) * self.scale)) + 1)
new_w = (int(((w - 1) * self.scale)) + 1)
... |
@mx.operator.register('BilinearScale')
class BilinearScaleProp(mx.operator.CustomOpProp):
def __init__(self, scale):
super(BilinearScaleProp, self).__init__(need_top_grad=True)
self.scale = float(scale)
def infer_shape(self, in_shape):
(n, c, h, w) = in_shape[0]
new_h = (int(... |
class BilinearScaleLike(mx.operator.CustomOp):
def forward(self, is_train, req, in_data, out_data, aux):
(x, x_ref) = in_data
(new_h, new_w) = x_ref.shape[2:]
x.attach_grad()
with mx.autograd.record():
new_x = mx.nd.contrib.BilinearResize2D(x, height=new_h, width=new_w... |
@mx.operator.register('BilinearScaleLike')
class BilinearScaleLikeProp(mx.operator.CustomOpProp):
def __init__(self):
super(BilinearScaleLikeProp, self).__init__(need_top_grad=True)
def list_arguments(self):
return ['d1', 'd2']
def infer_shape(self, in_shape):
out_shape = list(i... |
class SegmentLoss(mx.operator.CustomOp):
def __init__(self, has_grad_scale, onehot_label, grad_scale):
self.has_grad_scale = has_grad_scale
self.onehot_label = onehot_label
self.grad_scale = grad_scale
def forward(self, is_train, req, in_data, out_data, aux):
prediction = mx.... |
@mx.operator.register('SegmentLoss')
class SegmentLossProp(mx.operator.CustomOpProp):
def __init__(self, has_grad_scale=0, onehot_label=0, grad_scale=1):
super(SegmentLossProp, self).__init__(need_top_grad=False)
self.has_grad_scale = (int(has_grad_scale) > 0)
self.onehot_label = (int(one... |
class MultiSigmoidLoss(mx.operator.CustomOp):
def __init__(self, grad_scale):
self.grad_scale = grad_scale
def forward(self, is_train, req, in_data, out_data, aux):
(logit, label) = in_data
prediction = mx.nd.sigmoid(logit, axis=1)
self.assign(out_data[0], req[0], prediction)... |
@mx.operator.register('MultiSigmoidLoss')
class MultiSigmoidLossProp(mx.operator.CustomOpProp):
def __init__(self, grad_scale=1):
super(MultiSigmoidLossProp, self).__init__(need_top_grad=False)
self.grad_scale = float(grad_scale)
def list_arguments(self):
return ['data', 'label']
... |
class MultiSoftmaxLoss(mx.operator.CustomOp):
def forward(self, is_train, req, in_data, out_data, aux):
(logit, label) = in_data
prediction = mx.nd.softmax(logit, axis=1)
self.assign(out_data[0], req[0], prediction)
def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
... |
@mx.operator.register('MultiSoftmaxLoss')
class MultiSoftmaxLossProp(mx.operator.CustomOpProp):
def __init__(self):
super(MultiSoftmaxLossProp, self).__init__(need_top_grad=False)
def list_arguments(self):
return ['data', 'label']
def list_outputs(self):
return ['output']
d... |
def vgg16_deeplab(x, name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
x = ConvRelu(x, 64, (3, 3), pad=(1, 1), name=(name + 'conv1_1'), lr_mult=lr_mult, reuse=reuse)
x = ConvRelu(x, 64, (3, 3), pad=(1, 1), name=(name + 'conv1_2'), lr_mult=lr_mult, reuse=reuse)
x = Pool(x, kern... |
def vgg16_largefov(x, num_cls, name=None, lr_mult=10, reuse=None):
name = ('' if (name is None) else name)
x = vgg16_deeplab(x, name, lr_mult=1, reuse=reuse)
x = ConvRelu(x, 1024, (3, 3), dilate=(12, 12), pad=(12, 12), name=(name + 'fc6'), reuse=reuse)
x = Drop(x, 0.5, name=(name + 'drop6'))
x = C... |
def vgg16_aspp(x, num_cls, name=None, lr_mult=10, reuse=None):
name = ('' if (name is None) else name)
x_backbone = vgg16_deeplab(x, name, lr_mult=1, reuse=reuse)
x_aspp = []
for d in (6, 12, 18, 24):
x = ConvRelu(x_backbone, 1024, (3, 3), dilate=(d, d), pad=(d, d), name=(name + ('fc6_aspp%d' ... |
def vgg16_cam(x, num_cls, name=None, lr_mult=10, reuse=None):
name = ('' if (name is None) else name)
x = vgg16_deeplab(x, name, lr_mult=1, reuse=reuse)
x = ConvRelu(x, 1024, (3, 3), pad=(1, 1), name=(name + 'fc6'), reuse=reuse)
x = Drop(x, 0.5, name=(name + 'drop6'))
x = ConvRelu(x, 1024, (1, 1),... |
class _VOC_proto(object):
@staticmethod
def _get_palette():
def bitget(bit, idx):
return ((bit & (1 << idx)) > 0)
cmap = []
for i in range(256):
(r, g, b) = (0, 0, 0)
idx = i
for j in range(8):
r = (r | (bitget(idx, 0) <... |
def imwrite(filename, image):
dirname = os.path.dirname(filename)
if (not os.path.exists(dirname)):
try:
os.makedirs(dirname)
except:
pass
cv2.imwrite(filename, image)
|
def npsave(filename, data):
dirname = os.path.dirname(filename)
if (not os.path.exists(dirname)):
try:
os.makedirs(dirname)
except:
pass
np.save(filename, data)
|
def pkldump(filename, data):
dirname = os.path.dirname(filename)
if (not os.path.exists(dirname)):
try:
os.makedirs(dirname)
except:
pass
with open(filename, 'wb') as f:
pickle.dump(data, f)
|
def imhstack(images, height=None):
images = as_list(images)
images = list(map(image2C3, images))
if (height is None):
height = np.array([img.shape[0] for img in images]).max()
images = [resize_height(img, height) for img in images]
if (len(images) == 1):
return images[0]
images... |
def imvstack(images, width=None):
images = as_list(images)
images = list(map(image2C3, images))
if (width is None):
width = np.array([img.shape[1] for img in images]).max()
images = [resize_width(img, width) for img in images]
if (len(images) == 1):
return images[0]
images = [[... |
def as_list(data):
if (not isinstance(data, (list, tuple))):
return [data]
return list(data)
|
def image2C3(image):
if (image.ndim == 3):
return image
if (image.ndim == 2):
return np.repeat(image[(..., np.newaxis)], 3, axis=2)
raise ValueError('image.ndim = {}, invalid image.'.format(image.ndim))
|
def resize_height(image, height):
if (image.shape[0] == height):
return image
(h, w) = image.shape[:2]
width = ((height * w) // h)
image = cv2.resize(image, (width, height))
return image
|
def resize_width(image, width):
if (image.shape[1] == width):
return image
(h, w) = image.shape[:2]
height = ((width * h) // w)
image = cv2.resize(image, (width, height))
return image
|
def imtext(image, text, space=(3, 3), color=(0, 0, 0), thickness=1, fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1.0):
assert isinstance(text, str), type(text)
size = cv2.getTextSize(text, fontFace, fontScale, thickness)
image = cv2.putText(image, text, (space[0], (size[1] + space[1])), fontFace, fontScal... |
def setGPU(gpus):
len_gpus = len(gpus.split(','))
os.environ['CUDA_VISIBLE_DEVICES'] = gpus
gpus = ','.join(map(str, range(len_gpus)))
return gpus
|
def getTime():
return datetime.now().strftime('%m-%d %H:%M:%S')
|
class Timer(object):
curr_record = None
prev_record = None
@classmethod
def record(cls):
cls.prev_record = cls.curr_record
cls.curr_record = time.time()
@classmethod
def interval(cls):
if (cls.prev_record is None):
return 0
return (cls.curr_record ... |
def wrapColor(string, color):
try:
header = {'red': '\x1b[91m', 'green': '\x1b[92m', 'yellow': '\x1b[93m', 'blue': '\x1b[94m', 'purple': '\x1b[95m', 'cyan': '\x1b[96m', 'darkcyan': '\x1b[36m', 'bold': '\x1b[1m', 'underline': '\x1b[4m'}[color.lower()]
except KeyError:
raise ValueError('Unknown ... |
def info(logger, msg, color=None):
msg = ('[{}]'.format(getTime()) + msg)
if (logger is not None):
logger.info(msg)
if (color is not None):
msg = wrapColor(msg, color)
print(msg)
|
def summaryArgs(logger, args, color=None):
if isinstance(args, ModuleType):
args = vars(args)
keys = [key for key in args.keys() if (key[:2] != '__')]
keys.sort()
length = max([len(x) for x in keys])
msg = [(('{:<' + str(length)) + '}: {}').format(k, args[k]) for k in keys]
msg = ('\n'... |
def loadParams(filename):
data = mx.nd.load(filename)
(arg_params, aux_params) = ({}, {})
for (name, value) in data.items():
if (name[:3] == 'arg'):
arg_params[name[4:]] = value
elif (name[:3] == 'aux'):
aux_params[name[4:]] = value
if (len(arg_params) == 0):
... |
class SaveParams(object):
def __init__(self, model, snapshot, model_name, num_save=5):
self.model = model
self.snapshot = snapshot
self.model_name = model_name
self.num_save = num_save
self.save_params = []
def save(self, n_epoch):
self.save_params += [os.path... |
def getLogger(snapshot, model_name):
if (not os.path.exists(snapshot)):
os.makedirs(snapshot)
logging.basicConfig(filename=os.path.join(snapshot, (model_name + '.log')), level=logging.INFO)
logger = logging.getLogger()
return logger
|
class LrScheduler(object):
def __init__(self, method, init_lr, kwargs):
self.method = method
self.init_lr = init_lr
if (method == 'step'):
self.step_list = kwargs['step_list']
self.factor = kwargs['factor']
self.get = self._step
elif (method == ... |
class GradBuffer(object):
def __init__(self, model):
self.model = model
self.cache = None
def write(self):
if (self.cache is None):
self.cache = [[(None if (g is None) else g.copyto(g.context)) for g in g_list] for g_list in self.model._exec_group.grad_arrays]
els... |
def initNormal(mean, std, name, shape):
if name.endswith('_weight'):
return mx.nd.normal(mean, std, shape)
if name.endswith('_bias'):
return mx.nd.zeros(shape)
if name.endswith('_gamma'):
return mx.nd.ones(shape)
if name.endswith('_beta'):
return mx.nd.zeros(shape)
... |
def checkParams(mod, arg_params, aux_params, auto_fix=True, initializer=mx.init.Normal(0.01), logger=None):
arg_params = ({} if (arg_params is None) else arg_params)
aux_params = ({} if (aux_params is None) else aux_params)
arg_shapes = {name: array[0].shape for (name, array) in zip(mod._exec_group.param_... |
def compute_embeddings(dataset: str, architecture: str, seed: int, step: int, layer: int) -> np.ndarray:
'\n Compute the representations of a layer specified by the arguments and save to a npy file\n\n :param dataset: Dataset to compute embeddings for\n :param architecture: Model weights to load\n :pa... |
def get_filepath(dataset, architecture, seed, step, layer, folder=False):
'\n Get filepath for embedding of interest (in order to check whether it has already\n been computed\n '
if folder:
return os.path.join(EMBEDDING_PATH, dataset, architecture, str(seed), str(step), str(layer))
else:
... |
def get_string_filepath(dataset, architecture, seed, step, layer):
return '{head}/{dataset}/{architecture}/{seed}/{step}/{layer}'.format(head=EMBEDDING_PATH, dataset=dataset, architecture=architecture, seed=seed, step=step, layer=layer)
|
def get_embedding_folderpath(dataset: str, architecture: str, seed: int, step: int) -> pathlib.Path:
'\n Return path of folder containing embedding arrays corresponding to:\n - layers of model specified by architecture, seed and step\n - inputs from dataset\n\n Args:\n dataset (str): name of th... |
def get_checkpoint_filepath(architecture: str, seed: int, step: int) -> pathlib.Path:
'\n Return path to model checkpoint specified by architecture, seed and step\n\n Args:\n architecture (str): name of model architecture, eg "resnet18"\n seed (int): seed used to train model\n step (int... |
def initialise_model(architecture: str) -> nn.Module:
'\n Return initialised network of a given architecture\n Currently: only works for resnet18, resnet34, resnet50, resnet101, resnet152\n\n Args:\n architecture (str): name of model architecture, eg "resnet18"\n\n Returns:\n nn.Module: ... |
def initialise_dataset(dataset: str, sample_size: int, sample_seed: int, normalize=True):
'\n Return Dataset object corresponding to a dataset name\n\n Args:\n dataset (str): name of dataset\n sample_size (int): number of inputs to subsample\n sample_seed (int): seed to use when subsamp... |
def get_embedding_folder(dataset, architecture, seed, step, layer):
suffix = pathlib.Path(f'embeddings/{dataset}/{architecture}/{seed}/{step}/{layer}')
return (resources_path / suffix)
|
def load_embedding(dataset: str, architecture: str, seed: int, step: int, layer: int) -> np.ndarray:
folder_path = get_embedding_folder(dataset, architecture, seed, step, layer)
if (not os.path.exists(folder_path)):
print('Computing representations for model')
os.makedirs(folder_path)
... |
def score_pair_to_csv(rep1_dict: dict, rep2_dict: dict, filename: str, metrics: list) -> None:
'\n Compute metric distance between two representations and save it to a csv file\n\n Args:\n rep1_dict (dict): dictionary specifying configuration of representation 1, to load its representation from disk\... |
def score_local_pair(rep1: np.ndarray, rep2: np.ndarray, filename: str, metrics: list, metadata: dict={}) -> None:
'\n Compute metric distances between two representations (in numpy array format) and\n save results to a csv file\n\n Args:\n rep1 (np.ndarray): representation 1 to compare\n r... |
def cca_decomp(A, B):
'Computes CCA vectors, correlations, and transformed matrices\n requires a < n and b < n\n Args:\n A: np.array of size a x n where a is the number of neurons and n is the dataset size\n B: np.array of size b x n where b is the number of neurons and n is the dataset size\n... |
def mean_sq_cca_corr(rho):
'Compute mean squared CCA correlation\n :param rho: canonical correlation coefficients returned by cca_decomp(A,B)\n '
return (np.sum((rho * rho)) / len(rho))
|
def mean_cca_corr(rho):
'Compute mean CCA correlation\n :param rho: canonical correlation coefficients returned by cca_decomp(A,B)\n '
return (np.sum(rho) / len(rho))
|
def pwcca_dist(A, rho, transformed_a):
'Computes projection weighted CCA distance between A and B given the correlation\n coefficients rho and the transformed matrices after running CCA\n :param A: np.array of size a x n where a is the number of neurons and n is the dataset size\n :param B: np.array of s... |
def lin_cka_dist(A, B):
'\n Computes Linear CKA distance bewteen representations A and B\n '
similarity = (np.linalg.norm((B @ A.T), ord='fro') ** 2)
normalization = (np.linalg.norm((A @ A.T), ord='fro') * np.linalg.norm((B @ B.T), ord='fro'))
return (1 - (similarity / normalization))
|
def lin_cka_prime_dist(A, B):
'\n Computes Linear CKA prime distance bewteen representations A and B\n The version here is suited to a, b >> n\n '
if (A.shape[0] > A.shape[1]):
At_A = (A.T @ A)
Bt_B = (B.T @ B)
numerator = np.sum(((At_A - Bt_B) ** 2))
denominator = ((n... |
def procrustes(A, B):
'\n Computes Procrustes distance bewteen representations A and B\n '
A_sq_frob = np.sum((A ** 2))
B_sq_frob = np.sum((B ** 2))
nuc = np.linalg.norm((A @ B.T), ord='nuc')
return ((A_sq_frob + B_sq_frob) - (2 * nuc))
|
def get_acc_diff(row, scores_df, task_list):
score_row1 = scores_df.iloc[row['seed1']]
score_row2 = scores_df.iloc[row['seed2']]
for task in task_list:
acc1 = score_row1[task]
acc2 = score_row2[task]
row[f'{task}_diff'] = abs((acc1 - acc2))
return row
|
def rename_scores(scores_df):
scores_df = scores_df.rename(columns={'MNLI dev acc.': 'mnli_dev_acc', 'Lexical (entailed)': 'lex_ent', 'Subseq (entailed)': 'sub_ent', 'Constituent (entailed)': 'const_ent', 'Lexical (nonent)': 'lex_nonent', 'Subseq (nonent)': 'sub_nonent', 'Constituent (nonent)': 'const_nonent', 'O... |
def get_full_df(scores_path, dists_path, full_df_path):
scores_df = pd.read_csv(scores_path)[0:100]
scores_df = rename_scores(scores_df)
task_list = list(scores_df.columns[1:9])
print('got scores_df')
dists_df = pd.read_csv(dists_path)
print('got dists_df')
print('getting full_df, will tak... |
def feather_sub_df(df, task, ref_depth):
seeds = list(df.seed1.unique())
accs = [scores_df.iloc[seed][task] for seed in seeds]
acc_dict = dict(zip(seeds, accs))
best_seed = max(acc_dict, key=acc_dict.get)
sub_df = df[(((df.layer1 == ref_depth) & (df.layer2 == ref_depth)) & ((df.seed1 == best_seed)... |
def feather_sub_df(df, task, ref_depth):
seeds = list(df.seed1.unique())
accs = [scores_df.iloc[seed][task] for seed in seeds]
acc_dict = dict(zip(seeds, accs))
best_seed = max(acc_dict, key=acc_dict.get)
sub_df = df[(((df.layer1 == ref_depth) & (df.layer2 == ref_depth)) & ((df.seed1 == best_seed)... |
def get_probing_accuracy(data_dict, task, seed, depth):
'\n average accuracy of model finetuned with finetuning seed seed on mnli\n when probing layer layer on task\n '
return np.mean(data_dict[task][seed][(depth + 1)][0][0])
|
def get_full_df(scores_path, dists_path, full_df_path):
dists_df = pd.read_csv(dists_path)
print('got dists_df')
print('adding probing scores to get full_df')
full_df = dists_df
data_dict = pkl.load(open(scores_path, 'rb'))
for task in task_list:
task_diff_list = []
for (_, row... |
def best_probing_seed(task, ref_depth, list_ref_seeds):
data_dict = pkl.load(open(scores_path, 'rb'))
list_to_max = [np.mean(data_dict[task][seed][(ref_depth + 1)][0][0]) for seed in list_ref_seeds]
(idx, _) = max(enumerate(list_to_max), key=(lambda x: x[1]))
return list_ref_seeds[idx]
|
def layer_sub_df(df, ref_depth, ref_seed):
sub_df = df.loc[(((df['seed1'] == ref_seed) & (df['layer1'] == ref_depth)) | ((df['seed2'] == ref_seed) & (df['layer2'] == ref_depth)))].reset_index()
num_layers = 12
assert (len(sub_df) == (num_layers * 10))
return sub_df
|
def aggregate_rank_corrs(df, task, layer_depths, list_ref_seeds, METRICS, sub_df_fn):
rho = {metric: [] for metric in METRICS}
rho_p = {metric: [] for metric in METRICS}
tau = {metric: [] for metric in METRICS}
tau_p = {metric: [] for metric in METRICS}
bad_fracs = {metric: [] for metric in METRIC... |
def best_probing_seed(task, ref_depth, list_ref_seeds):
data_dict = pkl.load(open(scores_path, 'rb'))
list_to_max = [np.mean(data_dict[task][seed][(ref_depth + 1)][0][0]) for seed in list_ref_seeds]
(idx, _) = max(enumerate(list_to_max), key=(lambda x: x[1]))
return list_ref_seeds[idx]
|
def layer_sub_df(df, ref_depth, ref_seed):
sub_df = df.loc[(((df['seed1'] == ref_seed) & (df['layer1'] == ref_depth)) | ((df['seed2'] == ref_seed) & (df['layer2'] == ref_depth)))].reset_index()
num_layers = 12
assert (len(sub_df) == (num_layers * 10))
return sub_df
|
def aggregate_rank_corrs(df, task, layer_depths, list_ref_seeds, METRICS, sub_df_fn):
rho = {metric: [] for metric in METRICS}
rho_p = {metric: [] for metric in METRICS}
tau = {metric: [] for metric in METRICS}
tau_p = {metric: [] for metric in METRICS}
bad_fracs = {metric: [] for metric in METRIC... |
def get_acc(data_dict, task, seed, layer, dims, run='average'):
if (run == 'average'):
return np.mean(data_dict[task][seed][(layer + 1)][dims])
elif (run == 'std'):
return np.std(data_dict[task][seed][(layer + 1)][dims])
else:
return data_dict[task][seed][(layer + 1)][dims][run]
|
def get_acc_diff(data_dict, row):
acc1 = get_acc(data_dict, task=probe_task, seed=row['seed1'], layer=row['layer1'], dims=0, run='average')
acc2 = get_acc(data_dict, task=probe_task, seed=row['seed2'], layer=row['layer2'], dims=row['dims_deleted'], run='average')
return np.abs((acc1 - acc2))
|
def get_full_df(scores_path, dists_path, full_df_path):
dists_df = pd.read_csv(dists_path)
print('got dists_df')
full_df = pd.DataFrame(dists_df[(((dists_df['seed1'].isin(REF_SEEDS) & dists_df['seed2'].isin(REF_SEEDS)) & dists_df['layer1'].isin(LAYERS)) & dists_df['layer2'].isin(LAYERS))])
print('filt... |
def pca_sub_df(df, task, ref_depth):
data_dict = pkl.load(open(scores_path, 'rb'))
accs = [get_acc(data_dict, probe_task, seed, layer=ref_depth, dims=0, run='average') for seed in REF_SEEDS]
acc_dict = dict(zip(REF_SEEDS, accs))
best_seed = max(acc_dict, key=acc_dict.get)
sub_df = df[(((df.layer1 ... |
def pca_sub_df(df, task, ref_depth):
data_dict = pkl.load(open(scores_path, 'rb'))
accs = [get_acc(data_dict, probe_task, seed, layer=ref_depth, dims=0, run='average') for seed in REF_SEEDS]
acc_dict = dict(zip(REF_SEEDS, accs))
best_seed = max(acc_dict, key=acc_dict.get)
sub_df = df[(((df.layer1 ... |
def collect_scores(scores_path):
(model2correctness_tensor, data_dict) = pkl.load(open(scores_path, 'rb'))
guid_set = set()
for datapoint in data_dict:
guid_set.add(datapoint['guid'].split('-')[0])
acc_dict = {}
for test_set in guid_set:
test_set_idxes = [idx for (idx, d) in enumer... |
def get_accuracy(acc_dict, stress_test, pretraining_seed, finetuning_seed):
return acc_dict[stress_test][pretraining_seed][finetuning_seed]
|
def get_acc_diff(acc_dict, stress_test, pre_seed1, pre_seed2, fine_seed1, fine_seed2):
avg_acc1 = get_accuracy(acc_dict, stress_test, pre_seed1, fine_seed1)
avg_acc2 = get_accuracy(acc_dict, stress_test, pre_seed2, fine_seed2)
return np.abs((avg_acc2 - avg_acc1))
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.