repo stringlengths 2 99 | file stringlengths 14 239 | code stringlengths 20 3.99M | file_length int64 20 3.99M | avg_line_length float64 9.73 128 | max_line_length int64 11 86.4k | extension_type stringclasses 1
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VQ-Diffusion | VQ-Diffusion-main/image_synthesis/modeling/codecs/text_codec/tokenize.py | import torch
import torch.nn as nn
from image_synthesis.modeling.modules.clip.clip import tokenize
from image_synthesis.modeling.codecs.base_codec import BaseCodec
from image_synthesis.utils.misc import instantiate_from_config
class Tokenize(BaseCodec):
def __init__(self, context_length:int = 256,
... | 3,124 | 36.202381 | 104 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/modeling/models/conditional_dalle.py | # VQ-Diffusion
import torch
import math
from torch import nn
from image_synthesis.utils.misc import instantiate_from_config
import time
import numpy as np
from PIL import Image
import os
from torch.cuda.amp import autocast
class C_DALLE(nn.Module):
def __init__(
self,
*,
content_info={'ke... | 11,968 | 40.559028 | 154 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/modeling/models/unconditional_dalle.py | # VQ-Diffusion
import torch
import math
from torch import nn
from image_synthesis.utils.misc import instantiate_from_config
import time
import numpy as np
from PIL import Image
import os
from torch.cuda.amp import autocast
class UC_DALLE(nn.Module):
def __init__(
self,
*,
content_info={'k... | 8,216 | 35.52 | 138 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/modeling/models/dalle.py | # VQ-Diffusion
import torch
import math
from torch import nn
from image_synthesis.utils.misc import instantiate_from_config
import time
import numpy as np
from PIL import Image
import os
from torch.cuda.amp import autocast
class DALLE(nn.Module):
def __init__(
self,
*,
content_info={'key'... | 14,512 | 43.246951 | 154 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/modeling/embeddings/class_embedding.py | import torch
import torch.nn as nn
from .base_embedding import BaseEmbedding
class ClassEmbedding(BaseEmbedding):
def __init__(self,
num_embed=1000,
embed_dim=512,
identity=False,
trainable=True,
):
super().__init__()
self... | 899 | 26.272727 | 74 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/modeling/embeddings/dalle_mask_image_embedding.py | import torch
import torch.nn as nn
from .base_embedding import BaseEmbedding
class DalleMaskImageEmbedding(BaseEmbedding):
def __init__(self,
num_embed=8192,
spatial_size=[32, 32], # height and with
embed_dim=3968,
trainable=True,
... | 2,507 | 42.241379 | 173 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/modeling/embeddings/base_embedding.py | import torch
from torch import nn
class BaseEmbedding(nn.Module):
def get_loss(self):
return None
def forward(self, **kwargs):
raise NotImplementedError
def train(self, mode=True):
self.training = mode
if self.trainable and mode:
super().train()
retur... | 507 | 19.32 | 49 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/modeling/embeddings/clip_text_embedding.py | import torch
import torch.nn as nn
from image_synthesis.modeling.modules.clip import clip
from image_synthesis.modeling.modules.clip import model as clip_model
from .base_embedding import BaseEmbedding
class CLIPTextEmbedding(BaseEmbedding):
def __init__(self,
clip_name='ViT-B/32',
... | 3,423 | 37.47191 | 121 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/modeling/utils/misc.py | from numpy.core.fromnumeric import resize
from numpy.lib.function_base import kaiser
from numpy.lib.npyio import save
import torch
import random
import math
from image_synthesis.distributed.distributed import all_reduce, get_world_size
def logits_top_k(logits, filter_ratio = 0.5, minimum=1, pad_value=None):
logits... | 5,282 | 32.01875 | 114 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/modeling/transformers/diffusion_transformer.py | # VQ-Diffusion
import math
import torch
from torch import nn
import torch.nn.functional as F
from image_synthesis.utils.misc import instantiate_from_config
import numpy as np
from einops import rearrange
from image_synthesis.distributed.distributed import is_primary, get_rank
from inspect import isfunction
from torc... | 29,919 | 42.678832 | 166 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/modeling/transformers/transformer_utils.py | # VQ-Diffusion
import math
import torch
from torch import nn
import torch.nn.functional as F
from image_synthesis.utils.misc import instantiate_from_config
import numpy as np
from einops import rearrange
from image_synthesis.distributed.distributed import is_primary, get_rank
from inspect import isfunction
from torc... | 30,407 | 41 | 131 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/taming/lr_scheduler.py | import numpy as np
class LambdaWarmUpCosineScheduler:
"""
note: use with a base_lr of 1.0
"""
def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):
self.lr_warm_up_steps = warm_up_steps
self.lr_start = lr_start
self.lr_min = lr_min
... | 1,205 | 33.457143 | 114 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/taming/util.py | import os, hashlib
import requests
from tqdm import tqdm
URL_MAP = {
"vgg_lpips": "https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1"
}
CKPT_MAP = {
"vgg_lpips": "vgg.pth"
}
MD5_MAP = {
"vgg_lpips": "d507d7349b931f0638a25a48a722f98a"
}
def download(url, local_path, chunk_size=1024):
os.... | 4,777 | 29.240506 | 85 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/taming/modules/util.py | import torch
import torch.nn as nn
def count_params(model):
total_params = sum(p.numel() for p in model.parameters())
return total_params
class ActNorm(nn.Module):
def __init__(self, num_features, logdet=False, affine=True,
allow_reverse_init=False):
assert affine
super(... | 3,847 | 28.374046 | 85 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/taming/modules/vqvae/quantize.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch import einsum
from einops import rearrange
class VectorQuantizer(nn.Module):
"""
see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py
_____________________... | 13,259 | 39.181818 | 110 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/taming/modules/discriminator/model.py | import functools
import torch.nn as nn
from image_synthesis.taming.modules.util import ActNorm
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm') != -1:
nn.init.normal_(m.weight... | 2,566 | 36.75 | 116 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/taming/modules/misc/coord.py | import torch
class CoordStage(object):
def __init__(self, n_embed, down_factor):
self.n_embed = n_embed
self.down_factor = down_factor
def eval(self):
return self
def encode(self, c):
"""fake vqmodel interface"""
assert 0.0 <= c.min() and c.max() <= 1.0
b,c... | 904 | 27.28125 | 79 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/taming/modules/diffusionmodules/model.py | # pytorch_diffusion + derived encoder decoder
import math
import torch
import torch.nn as nn
import numpy as np
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This mat... | 30,221 | 37.895753 | 121 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/taming/modules/transformer/mingpt.py | """
taken from: https://github.com/karpathy/minGPT/
GPT model:
- the initial stem consists of a combination of token encoding and a positional encoding
- the meat of it is a uniform sequence of Transformer blocks
- each Transformer is a sequential combination of a 1-hidden-layer MLP block and a self-attention block... | 15,743 | 40.10705 | 140 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/taming/modules/transformer/permuter.py | import torch
import torch.nn as nn
import numpy as np
class AbstractPermuter(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
def forward(self, x, reverse=False):
raise NotImplementedError
class Identity(AbstractPermuter):
def __init__(self):
super().__init__()... | 7,093 | 27.48996 | 83 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/taming/modules/losses/lpips.py | """Stripped version of https://github.com/richzhang/PerceptualSimilarity/tree/master/models"""
import torch
import torch.nn as nn
from torchvision import models
from collections import namedtuple
from image_synthesis.taming.util import get_ckpt_path
class LPIPS(nn.Module):
# Learned perceptual metric
def __... | 4,778 | 38.172131 | 104 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/taming/modules/losses/segmentation.py | import torch.nn as nn
import torch.nn.functional as F
class BCELoss(nn.Module):
def forward(self, prediction, target):
loss = F.binary_cross_entropy_with_logits(prediction,target)
return loss, {}
class BCELossWithQuant(nn.Module):
def __init__(self, codebook_weight=1.):
super().__ini... | 816 | 34.521739 | 82 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/taming/modules/losses/vqperceptual.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from image_synthesis.taming.modules.losses.lpips import LPIPS
from image_synthesis.taming.modules.discriminator.model import NLayerDiscriminator, weights_init
class DummyLoss(nn.Module):
def __init__(self):
super().__init__()
def adopt_... | 6,211 | 44.343066 | 113 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/taming/models/vqgan.py | import torch
import torch.nn.functional as F
import pytorch_lightning as pl
from image_synthesis.utils.misc import instantiate_from_config
from image_synthesis.taming.modules.diffusionmodules.model import Encoder, Decoder
from image_synthesis.taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
fr... | 10,554 | 39.28626 | 120 | py |
VQ-Diffusion | VQ-Diffusion-main/image_synthesis/taming/models/cond_transformer.py | import os, math
import torch
import torch.nn.functional as F
import pytorch_lightning as pl
from image_synthesis.utils.misc import instantiate_from_config
from image_synthesis.taming.modules.util import SOSProvider
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure trai... | 15,049 | 42.75 | 127 | py |
VQ-Diffusion | VQ-Diffusion-main/running_command/run_train_ffhq.py | import os
string = "python train.py --name ffhq_train --config_file configs/ffhq.yaml --num_node 1 --tensorboard"
os.system(string)
| 135 | 18.428571 | 103 | py |
VQ-Diffusion | VQ-Diffusion-main/running_command/run_tune_coco.py | import os
string = "python train.py --name coco_tune --config_file configs/coco_tune.yaml --num_node 1 --tensorboard --load_path OUTPUT/pretrained_model/COCO_pretrained.pth"
os.system(string)
| 195 | 27 | 163 | py |
VQ-Diffusion | VQ-Diffusion-main/running_command/run_train_imagenet.py | import os
string = "python train.py --name imagenet_train --config_file configs/imagenet.yaml --num_node 1 --tensorboard"
os.system(string)
| 143 | 19.571429 | 111 | py |
VQ-Diffusion | VQ-Diffusion-main/running_command/run_train_coco.py | import os
string = "python train.py --name coco_train --config_file configs/coco.yaml --num_node 1 --tensorboard --load_path OUTPUT/pretrained_model/CC_pretrained.pth"
os.system(string)
| 189 | 26.142857 | 157 | py |
VQ-Diffusion | VQ-Diffusion-main/running_command/run_train_cub.py | import os
string = "python train.py --name cub200_train --config_file configs/cub200.yaml --num_node 1 --tensorboard --load_path OUTPUT/pretrained_model/CC_pretrained.pth"
os.system(string)
| 193 | 26.714286 | 161 | py |
Reflect | Reflect-master/mnist_trainer.py | import os
import tensorflow as tf
from util import constants
from util.config_util import get_model_params, get_task_params, get_train_params
from tf2_models.trainer import Trainer
from absl import app
from absl import flags
from util.models import MODELS
from util.tasks import TASKS
FLAGS = flags.FLAGS
flags.DEFINE_... | 2,498 | 31.454545 | 153 | py |
Reflect | Reflect-master/keras_trainer.py | import os
import tensorflow as tf
from util import constants
from util.config_util import get_model_params, get_task_params, get_train_params
from tf2_models.trainer import Trainer
from absl import app
from absl import flags
from util.models import MODELS
from util.tasks import TASKS
FLAGS = flags.FLAGS
flags.DEFINE_... | 2,688 | 34.381579 | 153 | py |
Reflect | Reflect-master/util/text_util.py | from collections import Counter
import csv
import subprocess
from util import inflect
import pandas as pd
from statsmodels.stats.proportion import proportion_confint
infl_eng = inflect.engine()
dependency_fields = ['sentence', 'orig_sentence', 'pos_sentence',
'subj', 'verb', 'subj_pos', 'has_rel',... | 4,707 | 30.178808 | 77 | py |
Reflect | Reflect-master/util/constants.py | pad = '<pad>'
unk = '<unk>'
bos = '<bos>'
eos = '<eos>'
pad_idx = 0
unk_idx = 1
bos_idx = 2
eos_idx = 3
all = [pad, unk, bos, eos] | 132 | 11.090909 | 26 | py |
Reflect | Reflect-master/util/inflect.py | '''
inflect.py: correctly generate plurals, ordinals, indefinite articles;
convert numbers to words
Copyright (C) 2010 Paul Dyson
Based upon the Perl module Lingua::EN::Inflect by Damian Conway.
This program is free software: you can redistribute it and/or modify
it under the terms o... | 94,476 | 30.273419 | 106 | py |
Reflect | Reflect-master/util/models.py | from tf2_models.capnet import Capsule
from tf2_models.cnn import VanillaCNN
from tf2_models.ff import VanillaFF
from tf2_models.ff_resnet import FFResnet
from tf2_models.lm_lstm import LmLSTM, LmLSTMSharedEmb, ClassifierLSTM, LmLSTMSharedEmbV2
from tf2_models.lm_transformer import LmGPT2, LmGPT2SharedWeights, Classifie... | 1,068 | 41.76 | 113 | py |
Reflect | Reflect-master/util/tasks.py | from tasks.lm1b import Lm1B
from tasks.mnist import Mnist, AffNistTask, Svhn, Mnist40
from tasks.smallnorb import SmallNorb
from tasks.sst import ClassifySST2, LmSST2
from tasks.sv_agreement import SvAgreementLM, WordSvAgreementLM, WordSvAgreementVP
from tasks.wiki import WikiLM
TASKS = {
'sv_agreement_lm': SvAgreem... | 606 | 27.904762 | 82 | py |
Reflect | Reflect-master/distill/offline_repshare.py | import tensorflow as tf
import os
from distill.distiller import Distiller
from distill.online_distiller import OnlineDistiller
from distill.repsim_util import get_reps
from tf2_models.train_utils import ExponentialDecayWithWarmpUp
from tf2_models.trainer import OPTIMIZER_DIC
from tf2_models.utils import camel2snake
fro... | 6,270 | 42.248276 | 128 | py |
Reflect | Reflect-master/distill/repsim_util.py | import tensorflow as tf
import numpy as np
def get_reps(outputs, index=1, layer=-1, **kwargs):
"""
If Model is LSTM:
1: final_rnn_outputs,
2: hidden_activation (for all layers, including input embeddings)
reduction: None, "last", "sum"
"""
logits = outputs[0]
outputs = tf.tuple(outputs)
rep... | 3,444 | 31.5 | 110 | py |
Reflect | Reflect-master/distill/online_distiller.py | import tensorflow as tf
import os
from distill.distill_util import get_distill_scheduler
from distill.distiller import Distiller
from tf2_models.train_utils import ExponentialDecayWithWarmpUp
from tf2_models.trainer import OPTIMIZER_DIC
from tf2_models.utils import camel2snake
from inspect import isfunction
import num... | 9,001 | 43.127451 | 132 | py |
Reflect | Reflect-master/distill/model.py | class Model(object):
def apply(self, examples):
raise NotImplementedError
def update(self, loss):
raise NotImplementedError | 136 | 21.833333 | 29 | py |
Reflect | Reflect-master/distill/distill_main.py | ''' Code to apply the distillation process for a teacher and a student model.
Run:
python distill/distill_main.py \
--task=word_sv_agreement_vp \
--teacher_exp_name=small_lstm_v4_0.0001_withl2 \
--teacher_model=cl_lstm \
--teacher_config=small_lstm_v4 \
--student_exp_name=distilled0 \
--student_model=cl_gpt2 \
--stude... | 5,174 | 47.820755 | 136 | py |
Reflect | Reflect-master/distill/distill_mnist.py | ''' Code to apply the distillation process for a teacher and a student model.
Run:
python distill/distill_main.py \
--task=word_sv_agreement_vp \
--teacher_exp_name=small_lstm_v4_0.0001_withl2 \
--teacher_model=cl_lstm \
--teacher_config=small_lstm_v4 \
--student_exp_name=distilled0 \
--student_model=cl_gpt2 \
--stude... | 4,778 | 47.272727 | 136 | py |
Reflect | Reflect-master/distill/distill_util.py | import tensorflow as tf
from tf2_models.metrics import distill_loss, sequence_distill_loss
@tf.function(experimental_relax_shapes=True)
def get_topk_mask(inputs, k):
inputs_shape = tf.shape(inputs)
inputs_shape = tf.cast(inputs_shape, dtype=tf.int64)
values, indices = tf.nn.top_k(inputs, k=k, sorted=False)
i... | 3,653 | 35.54 | 110 | py |
Reflect | Reflect-master/distill/distiller.py | import tensorflow as tf
import os
from distill.distill_util import get_distill_scheduler
from tf2_models.train_utils import ExponentialDecayWithWarmpUp
from tf2_models.trainer import OPTIMIZER_DIC
import numpy as np
class Distiller(object):
''' Pipeline for offline distillation.
'''
def __init__(self, hparams,... | 9,284 | 44.292683 | 132 | py |
Reflect | Reflect-master/tf2_models/embedding.py | import tensorflow as tf
from tf2_models.common_layers import get_initializer, shape_list
class SharedEmbeddings(tf.keras.layers.Layer):
"""Construct shared token embeddings.
"""
def __init__(self, vocab_size, hidden_size, initializer_range=None, regularizer=None, **kwargs):
super(SharedEmbeddings, self)._... | 2,633 | 38.313433 | 137 | py |
Reflect | Reflect-master/tf2_models/lm_transformer.py | import tensorflow as tf
from tf2_models.common_layers import get_initializer, shape_list
from tf2_models.embedding import SharedEmbeddings
from tf2_models.transformer_layers import Block
from tf2_models.transformers import *
class LmGPT2(tf.keras.Model):
def __init__(self, hparams, scope='lm_gpt2', *inputs, **kwargs... | 10,814 | 39.965909 | 109 | py |
Reflect | Reflect-master/tf2_models/ff.py | import tensorflow as tf
import numpy as np
class VanillaFF(tf.keras.models.Sequential):
def __init__(self, hparams, scope="cl_vff", *inputs, **kwargs):
if 'cl_token' in kwargs:
del kwargs['cl_token']
super(VanillaFF, self).__init__()
self.scope = scope
self.hparams = hparams
self.model_n... | 3,116 | 36.107143 | 92 | py |
Reflect | Reflect-master/tf2_models/common_layers.py | import tensorflow as tf
import numpy as np
from tensorflow.python.framework import tensor_shape
from tensorflow.python.util import nest
def gelu(x):
"""Gaussian Error Linear Unit.
This is a smoother version of the RELU.
Original paper: https://arxiv.org/abs/1606.08415
Args:
x: float Tensor to perform ac... | 3,398 | 34.041237 | 72 | py |
Reflect | Reflect-master/tf2_models/lm_lstm.py | import absl
import tensorflow as tf
import numpy as np
from tensorboard.compat.tensorflow_stub import tensor_shape
from tensorflow.python.util import nest
from tf2_models.common_layers import get_initializer
from tf2_models.embedding import SharedEmbeddings
from tf2_models.utils import create_init_var
class LmLSTM(tf... | 23,117 | 47.364017 | 138 | py |
Reflect | Reflect-master/tf2_models/transformers.py | import tensorflow as tf
from tf2_models.common_layers import get_initializer, shape_list
from tf2_models.embedding import SharedEmbeddings
from tf2_models.transformer_layers import Block
class GPT2(tf.keras.layers.Layer):
def __init__(self, hparams, *inputs, **kwargs):
super(GPT2, self).__init__(hparams, *input... | 16,938 | 40.619165 | 113 | py |
Reflect | Reflect-master/tf2_models/resnet.py | import tensorflow as tf
class ResnetBlock(tf.keras.layers.Layer):
def __init__(self, filters, kernel_size, activation='relu',*inputs, **kwargs):
super(ResnetBlock, self).__init__(*inputs, **kwargs)
self.filters = filters
self.kernel_size = kernel_size
self.activation = activation
self.regularizer... | 6,572 | 40.601266 | 94 | py |
Reflect | Reflect-master/tf2_models/cnn.py | import tensorflow as tf
import numpy as np
def max_out(inputs, num_units, axis=None):
shape = inputs.get_shape().as_list()
if shape[0] is None:
shape[0] = -1
if axis is None: # Assume that channel is the last dimension
axis = -1
num_channels = shape[axis]
if num_channels % num_units:
raise Valu... | 5,878 | 38.993197 | 91 | py |
Reflect | Reflect-master/tf2_models/utils.py | import tensorflow as tf
import re
from tensorboard.compat.tensorflow_stub import tensor_shape
def camel2snake(name):
return name[0].lower() + re.sub(r'(?!^)[A-Z]', lambda x: '_' + x.group(0).lower(), name[1:])
def log_summary(log_value, log_name, summary_scope):
"""Produce scalar summaries."""
with tf.compat.... | 884 | 31.777778 | 99 | py |
Reflect | Reflect-master/tf2_models/train_utils.py | import absl
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.keras.optimizer_v2.learning_rate_schedule import LearningRateSchedule
from tensorflow.python.ops import math_ops
from tensorflow.python.util.tf_export import keras_export
from tensorflow_addons.utils import keras_uti... | 17,416 | 40.568019 | 92 | py |
Reflect | Reflect-master/tf2_models/transformer_layers.py | import tensorflow as tf
from tf2_models.common_layers import get_initializer, shape_list, gelu
class Attention(tf.keras.layers.Layer):
def __init__(self, hidden_dim, n_ctx, config, regularizer, casual_masking=True, scale=False, **kwargs):
super(Attention, self).__init__(**kwargs)
self.output_attentions = c... | 6,560 | 35.049451 | 105 | py |
Reflect | Reflect-master/tf2_models/ff_resnet.py | import tensorflow as tf
class FFResnetBlock(tf.keras.layers.Layer):
def __init__(self, filters, kernel_size, activation='relu',*inputs, **kwargs):
super(FFResnetBlock, self).__init__(*inputs, **kwargs)
self.filters = filters
self.kernel_size = kernel_size
self.activation = activation
self.regular... | 6,118 | 39.256579 | 96 | py |
Reflect | Reflect-master/tf2_models/keras_callbacks.py | import tensorflow as tf
from tf2_models.utils import log_summary
class CheckpointCallback(tf.keras.callbacks.Callback):
def __init__(self, manager, ckpt):
super(CheckpointCallback, self).__init__()
self.manager = manager
self.ckpt = ckpt
def on_epoch_end(self, epoch, logs=None):
self.ckpt.step.... | 1,859 | 38.574468 | 148 | py |
Reflect | Reflect-master/tf2_models/metrics.py | import tensorflow as tf
@tf.function(experimental_relax_shapes=True)
def distill_loss(y_true, y_pred, tmp):
y_true = tf.cast(tf.squeeze(y_true), dtype=tf.float32)
scale_factor = 1.0 / (tmp*tmp)
return tf.reduce_mean(tf.compat.v2.nn.softmax_cross_entropy_with_logits(logits=y_pred / tmp,
... | 10,276 | 46.578704 | 117 | py |
Reflect | Reflect-master/tf2_models/trainer.py | import tensorflow as tf
import os
from tf2_models.keras_callbacks import CheckpointCallback, SummaryCallback
from tf2_models.train_utils import RectifiedAdam, ExponentialDecayWithWarmpUp
OPTIMIZER_DIC = {'adam': tf.keras.optimizers.Adam,
'radam': RectifiedAdam,
}
class Trainer(object)... | 3,931 | 39.536082 | 122 | py |
Reflect | Reflect-master/tfds_data/tal_agreement.py | from collections import Counter
import tensorflow as tf
import tensorflow_datasets as tfds
import os
import numpy as np
from tensorflow_datasets.core.features.text import Tokenizer
from tensorflow_datasets.core.features.text.text_encoder import write_lines_to_file, read_lines_from_file
from prep_data.build_dictionary... | 8,680 | 35.020747 | 106 | py |
Reflect | Reflect-master/tasks/task.py | import tensorflow as tf
from distill.distill_util import get_masked_probs
from distill.repsim_util import rep_loss
from util import constants
class Task(object):
def __init__(self, task_params, num_replicas_in_sync=1, builder_cls=None, name='abstract_task', data_dir='data', output_padding=False):
self.name = na... | 6,704 | 47.586957 | 142 | py |
Reflect | Reflect-master/tasks/sv_agreement.py | import functools
from distill.distill_util import DistillLoss, get_probs, SequenceDistillLoss, get_topk_masked_probs, get_masked_probs
from tasks.task import Task
import tensorflow as tf
from tf2_models import metrics
from tf2_models.metrics import masked_batch_perplexity, masked_perplexity, \
MaskedSequenceLoss, C... | 5,424 | 44.208333 | 163 | py |
Reflect | Reflect-master/tasks/mnist.py | from distill.distill_util import DistillLoss, get_probs
from tasks.task import Task
import tensorflow as tf
import tensorflow_datasets as tfds
from tf2_models.metrics import ClassificationLoss
from tfds_data.aff_nist import AffNist
class Mnist(Task):
def __init__(self, task_params, name='mnist', data_dir='mnist_da... | 8,663 | 37.678571 | 103 | py |
Reflect | Reflect-master/tasks/evaluations/lm_sv_agreement_eval.py | ''' Evaluate word based language models on the subject verb agreement task.
Codes adapted from:
Example Run:
python tasks/evaluations/lm_sv_agreement_eval.py \
--exp_name=lisa_fd4 \
--model_name=lm_gpt2 \
--model_config=very_big_gpt_v10 \
--train_config=adam_slow \
--prefix=offline_pure_distill_2_teacher_lm_lstm_shar... | 6,755 | 36.955056 | 135 | py |
Reflect | Reflect-master/notebooks/notebook_utils.py | import tensorflow as tf
import numpy as np
import os
from tqdm import tqdm
from util import constants
from collections import Counter
from util.models import MODELS
from util.tasks import TASKS
from util.config_util import get_model_params, get_task_params, get_train_params
import matplotlib.pyplot as plt
import pandas... | 10,989 | 36.508532 | 153 | py |
Reflect | Reflect-master/notebooks/calibration_util.py | import os
import tensorflow as tf
from util import constants
from util.config_util import get_model_params, get_task_params, get_train_params
from tf2_models.trainer import Trainer
from absl import app
from absl import flags
import numpy as np
from util.models import MODELS
from util.tasks import TASKS
import tensorflo... | 3,258 | 38.26506 | 100 | py |
Reflect | Reflect-master/notebooks/eval_scripts/eval_vp.py | import os
import tensorflow as tf
from util import constants
from util.config_util import get_model_params, get_task_params, get_train_params
from tf2_models.trainer import Trainer
from absl import app
from absl import flags
import numpy as np
from util.models import MODELS
from util.tasks import TASKS
from notebook_ut... | 5,193 | 28.68 | 139 | py |
Reflect | Reflect-master/notebooks/eval_scripts/eval_vp-bert.py | import os
import tensorflow as tf
from util import constants
from util.config_util import get_model_params, get_task_params, get_train_params
from tf2_models.trainer import Trainer
from absl import app
from absl import flags
import numpy as np
from util.models import MODELS
from util.tasks import TASKS
from notebook_ut... | 19,508 | 33.962366 | 137 | py |
Reflect | Reflect-master/notebooks/eval_scripts/eval_vp-ugpt.py | import os
import tensorflow as tf
from util import constants
from util.config_util import get_model_params, get_task_params, get_train_params
from tf2_models.trainer import Trainer
from absl import app
from absl import flags
import numpy as np
from util.models import MODELS
from util.tasks import TASKS
from notebook_ut... | 19,655 | 33.851064 | 139 | py |
Reflect | Reflect-master/notebooks/eval_scripts/eval_vp-lstm.py | import os
import tensorflow as tf
from util import constants
from util.config_util import get_model_params, get_task_params, get_train_params
from tf2_models.trainer import Trainer
from absl import app
from absl import flags
import numpy as np
from util.models import MODELS
from util.tasks import TASKS
from notebook_ut... | 7,129 | 32.009259 | 139 | py |
Reflect | Reflect-master/notebooks/eval_scripts/eval_full_sv_cl.py | import os
import tensorflow as tf
from util import constants
from util.config_util import get_model_params, get_task_params, get_train_params
from tf2_models.trainer import Trainer
from absl import app
from absl import flags
import numpy as np
from util.models import MODELS
from util.tasks import TASKS
from notebook_ut... | 4,451 | 29.285714 | 108 | py |
Reflect | Reflect-master/notebooks/eval_scripts/eval_lm.py | import os
import tensorflow as tf
from util import constants
from util.config_util import get_model_params, get_task_params, get_train_params
from tf2_models.trainer import Trainer
from absl import app
from absl import flags
import numpy as np
from util.models import MODELS
from util.tasks import TASKS
from notebook_ut... | 2,524 | 28.360465 | 86 | py |
Reflect | Reflect-master/notebooks/eval_scripts/eval_full_sv_cl_gpt2.py | import os
import tensorflow as tf
from util import constants
from util.config_util import get_model_params, get_task_params, get_train_params
from tf2_models.trainer import Trainer
from absl import app
from absl import flags
import numpy as np
from util.models import MODELS
from util.tasks import TASKS
from notebook_ut... | 4,517 | 29.322148 | 108 | py |
Reflect | Reflect-master/notebooks/eval_scripts/eval_full_sv_cl_bert.py | import os
import tensorflow as tf
from util import constants
from util.config_util import get_model_params, get_task_params, get_train_params
from tf2_models.trainer import Trainer
from absl import app
from absl import flags
import numpy as np
from util.models import MODELS
from util.tasks import TASKS
from notebook_ut... | 4,447 | 29.258503 | 108 | py |
Reflect | Reflect-master/prep_data/split.py | import sys
import os
import errno
import random
from util.text_util import deps_from_tsv, deps_to_tsv
def make_splits(fname, expr_dir, prop_train=0.1, prop_valid=0.01):
# for reproducibility
random.seed(42)
print('| read in the data')
data = deps_from_tsv(fname)
print('| shuffling')
random.sh... | 947 | 25.333333 | 66 | py |
Reflect | Reflect-master/prep_data/gen_bowman_logic.py | from itertools import chain
from itertools import combinations
from collections import Counter
import random
def powerset(iterable):
s = list(iterable)
return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1))
def get_candidate_worlds(num_vars):
return powerset(set(range(num_vars)))
de... | 5,553 | 28.386243 | 77 | py |
Reflect | Reflect-master/prep_data/build_dictionary.py | from util import text_util as utils
from util import constants
from sys import argv
import numpy as np
import os
def build_and_save_dic(input_file, data_dir):
worddict = {}
worddict[constants.pad] = constants.pad_idx
worddict[constants.unk] = constants.unk_idx
worddict[constants.bos] = constants.bos_i... | 969 | 26.714286 | 51 | py |
PyKrige | PyKrige-main/setup.py | """Kriging Toolkit for Python."""
import os
import numpy as np
from Cython.Build import cythonize
from setuptools import Extension, setup
# cython extensions
CY_MODULES = [
Extension(
name=f"pykrige.{ext}",
sources=[os.path.join("src", "pykrige", *ext.split(".")) + ".pyx"],
include_dirs=[n... | 634 | 27.863636 | 75 | py |
PyKrige | PyKrige-main/benchmarks/kriging_benchmarks.py | """Benchmarks."""
from time import time
import numpy as np
from pykrige.ok import OrdinaryKriging
np.random.seed(19999)
VARIOGRAM_MODELS = ["power", "gaussian", "spherical", "exponential", "linear"]
BACKENDS = ["vectorized", "loop", "C"]
N_MOVING_WINDOW = [None, 10, 50, 100]
def make_benchark(n_train, n_test, n_d... | 3,473 | 27.47541 | 88 | py |
PyKrige | PyKrige-main/examples/06_exact_values_example_1D.py | """
Exact Values
============
PyKrige demonstration and usage
as a non-exact interpolator in 1D.
"""
import matplotlib.pyplot as plt
import numpy as np
from pykrige.ok import OrdinaryKriging
plt.style.use("ggplot")
np.random.seed(42)
x = np.linspace(0, 12.5, 50)
xpred = np.linspace(0, 12.5, 393)
y = np.sin(x) * ... | 1,375 | 21.557377 | 77 | py |
PyKrige | PyKrige-main/examples/00_ordinary.py | """
Ordinary Kriging Example
========================
First we will create a 2D dataset together with the associated x, y grids.
"""
import matplotlib.pyplot as plt
import numpy as np
import pykrige.kriging_tools as kt
from pykrige.ok import OrdinaryKriging
data = np.array(
[
[0.3, 1.2, 0.47],
... | 1,840 | 30.20339 | 84 | py |
PyKrige | PyKrige-main/examples/07_regression_kriging2d.py | """
Regression kriging
------------------
An example of regression kriging
"""
import sys
from sklearn.datasets import fetch_california_housing
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.svm im... | 1,368 | 28.76087 | 81 | py |
PyKrige | PyKrige-main/examples/10_classification_kriging2d.py | """
Classification kriging
----------------------
An example of classification kriging
"""
import sys
from sklearn.datasets import fetch_california_housing
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from... | 1,566 | 29.72549 | 86 | py |
PyKrige | PyKrige-main/examples/01_universal.py | """
Universal Kriging Example
=========================
In this example we apply a regional linear trend to the kriging system.
"""
import matplotlib.pyplot as plt
import numpy as np
from pykrige.uk import UniversalKriging
data = np.array(
[
[0.3, 1.2, 0.47],
[1.9, 0.6, 0.56],
[1.1, 3.2... | 1,475 | 27.941176 | 84 | py |
PyKrige | PyKrige-main/examples/08_krige_cv.py | """
Krige CV
--------
Searching for optimal kriging parameters with cross validation
"""
import numpy as np
from sklearn.model_selection import GridSearchCV
from pykrige.rk import Krige
# 2D Kring param opt
param_dict = {
"method": ["ordinary", "universal"],
"variogram_model": ["linear", "power", "gaussian... | 1,951 | 23.708861 | 86 | py |
PyKrige | PyKrige-main/examples/02_kriging3D.py | """
Three-Dimensional Kriging Example
=================================
"""
import numpy as np
from matplotlib import pyplot as plt
from pykrige.ok3d import OrdinaryKriging3D
from pykrige.uk3d import UniversalKriging3D
data = np.array(
[
[0.1, 0.1, 0.3, 0.9],
[0.2, 0.1, 0.4, 0.8],
[0.1, ... | 3,514 | 32.47619 | 85 | py |
PyKrige | PyKrige-main/examples/05_kriging_1D.py | """
1D Kriging
==========
An example of 1D kriging with PyKrige
"""
import matplotlib.pyplot as plt
import numpy as np
from pykrige import OrdinaryKriging
plt.style.use("ggplot")
# fmt: off
# Data taken from
X, y = np.array([
[-5.01, 1.06], [-4.90, 0.92], [-4.82, 0.35], [-4.69, 0.49], [-4.56, 0.52],
[-4.5... | 2,626 | 36 | 79 | py |
PyKrige | PyKrige-main/examples/04_krige_geometric.py | """
Geometric example
=================
A small example script showing the usage of the 'geographic' coordinates type
for ordinary kriging on a sphere.
"""
import numpy as np
from matplotlib import pyplot as plt
from pykrige.ok import OrdinaryKriging
# Make this example reproducible:
np.random.seed(89239413)
# Gen... | 2,636 | 31.555556 | 79 | py |
PyKrige | PyKrige-main/examples/03_gstools_covmodel.py | """
GSTools Interface
=================
Example how to use the PyKrige routines with a GSTools CovModel.
"""
import gstools as gs
import numpy as np
from matplotlib import pyplot as plt
from pykrige.ok import OrdinaryKriging
# conditioning data
data = np.array(
[
[0.3, 1.2, 0.47],
[1.9, 0.6, 0.56... | 844 | 23.852941 | 87 | py |
PyKrige | PyKrige-main/src/pykrige/ok.py | """
PyKrige
=======
Code by Benjamin S. Murphy and the PyKrige Developers
bscott.murphy@gmail.com
Summary
-------
Contains class OrdinaryKriging, which provides easy access to
2D Ordinary Kriging.
References
----------
.. [1] P.K. Kitanidis, Introduction to Geostatistcs: Applications in
Hydrogeology, (Cambridge ... | 42,554 | 40.679726 | 88 | py |
PyKrige | PyKrige-main/src/pykrige/compat_gstools.py | # pylint: disable= invalid-name, unused-import
"""For GSTools compatibility."""
# gstools
try:
import gstools as gs
GSTOOLS_INSTALLED = True
GSTOOLS_VERSION = list(map(int, gs.__version__.split(".")[:2]))
except ImportError:
gs = None
GSTOOLS_INSTALLED = False
GSTOOLS_VERSION = None
class G... | 1,062 | 27.72973 | 85 | py |
PyKrige | PyKrige-main/src/pykrige/uk.py | """
PyKrige
=======
Code by Benjamin S. Murphy and the PyKrige Developers
bscott.murphy@gmail.com
Summary
-------
Contains class UniversalKriging, provides greater control over 2D kriging by
utilizing drift terms.
References
----------
.. [1] P.K. Kitanidis, Introduction to Geostatistcs: Applications in
Hydrogeo... | 56,799 | 41.706767 | 87 | py |
PyKrige | PyKrige-main/src/pykrige/core.py | """
PyKrige
=======
Code by Benjamin S. Murphy and the PyKrige Developers
bscott.murphy@gmail.com
Summary
-------
Methods used by multiple classes.
References
----------
[1] P.K. Kitanidis, Introduction to Geostatistcs: Applications in Hydrogeology,
(Cambridge University Press, 1997) 272 p.
[2] T. Vincenty, Dir... | 30,289 | 33.538198 | 88 | py |
PyKrige | PyKrige-main/src/pykrige/uk3d.py | """
PyKrige
=======
Code by Benjamin S. Murphy and the PyKrige Developers
bscott.murphy@gmail.com
Summary
-------
Contains class UniversalKriging3D.
References
----------
.. [1] P.K. Kitanidis, Introduction to Geostatistcs: Applications in
Hydrogeology, (Cambridge University Press, 1997) 272 p.
.. [2] N. Cressie... | 49,151 | 41.852659 | 88 | py |
PyKrige | PyKrige-main/src/pykrige/ok3d.py | """
PyKrige
=======
Code by Benjamin S. Murphy and the PyKrige Developers
bscott.murphy@gmail.com
Summary
-------
Contains class OrdinaryKriging3D.
References
----------
.. [1] P.K. Kitanidis, Introduction to Geostatistcs: Applications in
Hydrogeology, (Cambridge University Press, 1997) 272 p.
.. [2] N. Cressie,... | 39,816 | 41.676313 | 88 | py |
PyKrige | PyKrige-main/src/pykrige/rk.py | """Regression Kriging."""
from pykrige.compat import Krige, check_sklearn_model, validate_sklearn
validate_sklearn()
from sklearn.metrics import r2_score
from sklearn.svm import SVR
class RegressionKriging:
"""
An implementation of Regression-Kriging.
As described here:
https://en.wikipedia.org/wik... | 5,982 | 30.994652 | 86 | py |
PyKrige | PyKrige-main/src/pykrige/variogram_models.py | """
PyKrige
=======
Code by Benjamin S. Murphy and the PyKrige Developers
bscott.murphy@gmail.com
Summary
-------
Function definitions for variogram models. In each function, m is a list of
defining parameters and d is an array of the distance values at which to
calculate the variogram model.
References
----------
.... | 2,092 | 24.52439 | 83 | py |
PyKrige | PyKrige-main/src/pykrige/ck.py | """Classification Kriging."""
import numpy as np
from pykrige.compat import Krige, check_sklearn_model, validate_sklearn
validate_sklearn()
from scipy.linalg import helmert
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import OneHotEncoder
from sklearn.svm import SVC
class ClassificationKri... | 9,458 | 31.393836 | 106 | py |
PyKrige | PyKrige-main/src/pykrige/compat.py | # pylint: disable= invalid-name, unused-import
"""For compatibility."""
from pykrige.ok import OrdinaryKriging
from pykrige.ok3d import OrdinaryKriging3D
from pykrige.uk import UniversalKriging
from pykrige.uk3d import UniversalKriging3D
# sklearn
try:
# keep train_test_split here for backward compatibility
f... | 9,889 | 31.11039 | 88 | py |
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