repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/relation_heads/auxilary/multi_head_att.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask == 0, -1e9)
s... | 1,719 | 28.152542 | 78 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/rpn/inference.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from lib.scene_parser.rcnn.modeling.box_coder import BoxCoder
from lib.scene_parser.rcnn.structures.bounding_box import BoxList
from lib.scene_parser.rcnn.structures.boxlist_ops import cat_boxlist
from lib.scene_parser.rcnn.structures... | 7,773 | 36.555556 | 87 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/rpn/anchor_generator.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import math
import numpy as np
import torch
from torch import nn
from lib.scene_parser.rcnn.structures.bounding_box import BoxList
class BufferList(nn.Module):
"""
Similar to nn.ParameterList, but for buffers
"""
def __init__(s... | 9,951 | 33.317241 | 88 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/rpn/loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
This file contains specific functions for computing losses on the RPN
file
"""
import torch
from torch.nn import functional as F
from .utils import concat_box_prediction_layers
from ..balanced_positive_negative_sampler import BalancedPositiv... | 5,780 | 35.588608 | 87 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/rpn/utils.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Utility functions minipulating the prediction layers
"""
from ..utils import cat
import torch
def permute_and_flatten(layer, N, A, C, H, W):
layer = layer.view(N, -1, C, H, W)
layer = layer.permute(0, 3, 4, 1, 2)
layer = layer.re... | 1,679 | 35.521739 | 80 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/rpn/rpn.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torch.nn.functional as F
from torch import nn
from lib.scene_parser.rcnn.modeling import registry
from lib.scene_parser.rcnn.modeling.box_coder import BoxCoder
from lib.scene_parser.rcnn.modeling.rpn.retinanet.retinanet import ... | 7,624 | 35.658654 | 88 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/rpn/retinanet/inference.py | import torch
from ..inference import RPNPostProcessor
from ..utils import permute_and_flatten
from lib.scene_parser.rcnn.modeling.box_coder import BoxCoder
from lib.scene_parser.rcnn.modeling.utils import cat
from lib.scene_parser.rcnn.structures.bounding_box import BoxList
from lib.scene_parser.rcnn.structures.boxli... | 6,937 | 34.579487 | 79 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/rpn/retinanet/loss.py | """
This file contains specific functions for computing losses on the RetinaNet
file
"""
import torch
from torch.nn import functional as F
from ..utils import concat_box_prediction_layers
from lib.scene_parser.rcnn.layers import smooth_l1_loss
from lib.scene_parser.rcnn.layers import SigmoidFocalLoss
from lib.scene_... | 3,505 | 31.462963 | 83 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/rpn/retinanet/retinanet.py | import math
import torch
import torch.nn.functional as F
from torch import nn
from .inference import make_retinanet_postprocessor
from .loss import make_retinanet_loss_evaluator
from ..anchor_generator import make_anchor_generator_retinanet
from lib.scene_parser.rcnn.modeling.box_coder import BoxCoder
class Retina... | 5,303 | 33.666667 | 88 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/roi_heads/roi_heads.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .box_head.box_head import build_roi_box_head
class CombinedROIHeads(torch.nn.ModuleDict):
"""
Combines a set of individual heads (for box prediction or masks) into a single
head.
"""
def __init__(self, cfg,... | 1,436 | 33.214286 | 93 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/roi_heads/box_head/inference.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torch.nn.functional as F
from torch import nn
from lib.scene_parser.rcnn.structures.bounding_box import BoxList
from lib.scene_parser.rcnn.structures.boxlist_ops import boxlist_nms
from lib.scene_parser.rcnn.structures.boxlist_... | 12,133 | 41.575439 | 108 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/roi_heads/box_head/roi_box_feature_extractors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from torch.nn import functional as F
from lib.scene_parser.rcnn.modeling import registry
from lib.scene_parser.rcnn.modeling.backbone import resnet
from lib.scene_parser.rcnn.modeling.poolers import Pooler
from li... | 5,419 | 34.657895 | 81 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/roi_heads/box_head/box_head.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from .roi_box_feature_extractors import make_roi_box_feature_extractor
from .roi_box_predictors import make_roi_box_predictor
from .inference import make_roi_box_post_processor
from .loss import make_roi_box_loss_... | 3,495 | 39.651163 | 96 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/roi_heads/box_head/loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch.nn import functional as F
from lib.scene_parser.rcnn.layers import smooth_l1_loss
from lib.scene_parser.rcnn.modeling.box_coder import BoxCoder
from lib.scene_parser.rcnn.modeling.matcher import Matcher
from lib.scene_parse... | 8,001 | 35.538813 | 102 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/modeling/roi_heads/box_head/roi_box_predictors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from lib.scene_parser.rcnn.modeling import registry
from torch import nn
@registry.ROI_BOX_PREDICTOR.register("FastRCNNPredictor")
class FastRCNNPredictor(nn.Module):
def __init__(self, config, in_channels):
super(FastRCNNPredictor, s... | 2,298 | 35.492063 | 87 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/structures/image_list.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from __future__ import division
import torch
class ImageList(object):
"""
Structure that holds a list of images (of possibly
varying sizes) as a single tensor.
This works by padding the images to the same size,
and storing in... | 2,485 | 33.054795 | 87 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/structures/segmentation_mask.py | import cv2
import copy
import torch
import numpy as np
from maskrcnn_benchmark.layers.misc import interpolate
from maskrcnn_benchmark.utils import cv2_util
import pycocotools.mask as mask_utils
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
""" ABSTRACT
Segmentations come in either:
1) Binary masks
2) Polygons
... | 18,637 | 31.357639 | 94 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/structures/bounding_box.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
class BoxList(object):
"""
This class represents a set of bounding boxes.
The bounding boxes are represented as a Nx4 Tensor.
In order to uniquely determine the bou... | 9,645 | 35.127341 | 92 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/structures/bounding_box_pair.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .bounding_box import BoxList
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
class BoxPairList(object):
"""
This class represents a set of bounding boxes.
The bounding boxes are represented as a Nx4 Tensor.
I... | 10,466 | 35.34375 | 97 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/structures/boxlist_ops.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .bounding_box import BoxList
from ..layers import nms as _box_nms
def boxlist_nms(boxlist, nms_thresh, max_proposals=-1, score_field="scores"):
"""
Performs non-maximum suppression on a boxlist, with scores specified
... | 3,703 | 27.492308 | 97 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/scene_parser/rcnn/structures/keypoint.py | import torch
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
class Keypoints(object):
def __init__(self, keypoints, size, mode=None):
# FIXME remove check once we have better integration with device
# in my version this would consistently return a CPU tensor
device = keypoints.device ... | 6,555 | 33.687831 | 97 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/utils/box.py | import numpy as np
import torch
def bbox_overlaps(anchors, gt_boxes):
"""
anchors: (N, 4) ndarray of float
gt_boxes: (K, 4) ndarray of float
overlaps: (N, K) ndarray of overlap between boxes and query_boxes
"""
N = anchors.size(0)
K = gt_boxes.size(0)
gt_boxes_area = ((gt_boxes[:,2] - ... | 998 | 28.382353 | 69 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/utils/pytorch_misc.py | """
Miscellaneous functions that might be useful for pytorch
"""
import h5py
import numpy as np
import torch
from torch.autograd import Variable
import os
import dill as pkl
from itertools import tee
from torch import nn
def optimistic_restore(network, state_dict):
mismatch = False
own_state = network.state_d... | 14,457 | 30.430435 | 110 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/vg_hdf5.py | import os
from collections import defaultdict
import numpy as np
import copy
import pickle
import scipy.sparse
from PIL import Image
import h5py, json
import torch
from pycocotools.coco import COCO
from torch.utils.data import Dataset
from lib.scene_parser.rcnn.structures.bounding_box import BoxList
from lib.utils.box ... | 12,110 | 40.618557 | 129 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/build.py | import copy
import bisect
import torch
from torch.utils import data
from .vg_hdf5 import vg_hdf5
from . import samplers
from .transforms import build_transforms
from .collate_batch import BatchCollator
from lib.scene_parser.rcnn.utils.comm import get_world_size, get_rank
def make_data_sampler(dataset, shuffle, distrib... | 3,399 | 40.463415 | 108 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/evaluation/gqa_coco/gqa_coco_eval.py | import logging
import tempfile
import os
import torch
from collections import OrderedDict
from tqdm import tqdm
from lib.scene_parser.mask_rcnn.modeling.roi_heads.mask_head.inference import Masker
from lib.scene_parser.mask_rcnn.structures.bounding_box import BoxList
from lib.scene_parser.mask_rcnn.structures.boxlist_... | 10,982 | 34.201923 | 91 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/evaluation/sg/sg_eval.py | import numpy as np
import torch
from .evaluator import BasicSceneGraphEvaluator
def do_sg_evaluation(dataset, predictions, predictions_pred, output_folder, logger):
"""
scene graph generation evaluation
"""
evaluator = BasicSceneGraphEvaluator.all_modes(multiple_preds=False)
top_Ns = [20, 50, 100... | 13,320 | 39.244713 | 108 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/evaluation/sg/evaluator.py | """
Adapted from Danfei Xu. In particular, slow code was removed
"""
import torch
import numpy as np
from functools import reduce
from lib.utils.pytorch_misc import intersect_2d, argsort_desc
from lib.utils.box import bbox_overlaps
MODES = ('sgdet', 'sgcls', 'predcls')
np.set_printoptions(precision=3)
class BasicSce... | 12,230 | 40.744027 | 139 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/evaluation/coco/coco_eval.py | import logging
import tempfile
import os
import torch
from collections import OrderedDict
from tqdm import tqdm
# from lib.scene_parser.rcnn.modeling.roi_heads.mask_head.inference import Masker
from lib.scene_parser.rcnn.structures.bounding_box import BoxList
from lib.scene_parser.rcnn.structures.boxlist_ops import bo... | 14,329 | 34.914787 | 89 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/samplers/grouped_batch_sampler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import itertools
import torch
from torch.utils.data.sampler import BatchSampler
from torch.utils.data.sampler import Sampler
class GroupedBatchSampler(BatchSampler):
"""
Wraps another sampler to yield a mini-batch of indices.
It enfo... | 4,845 | 40.775862 | 88 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/samplers/iteration_based_batch_sampler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from torch.utils.data.sampler import BatchSampler
class IterationBasedBatchSampler(BatchSampler):
"""
Wraps a BatchSampler, resampling from it until
a specified number of iterations have been sampled
"""
def __init__(self, ba... | 1,164 | 35.40625 | 71 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/samplers/distributed.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Code is copy-pasted exactly as in torch.utils.data.distributed.
# FIXME remove this once c10d fixes the bug it has
import math
import torch
import torch.distributed as dist
from torch.utils.data.sampler import Sampler
class DistributedSampler(S... | 2,569 | 37.358209 | 86 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/transforms/transforms.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import random
import torch
import torchvision
from torchvision.transforms import functional as F
class Compose(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, image, target):
for ... | 3,477 | 27.508197 | 83 | py |
DMCrypt | DMCrypt-main/main.py | import torch
import torch.nn as nn
import pandas as pd
import numpy as np
from torch.utils.data import Dataset, DataLoader
from torch.autograd import Variable
from sklearn.preprocessing import MinMaxScaler, StandardScaler
#import seaborn as sns
import matplotlib.pyplot as plt
import pickle5 as pickle
import sys
import... | 641 | 28.181818 | 97 | py |
DMCrypt | DMCrypt-main/model/AdaBoost-LSTM.py | import torch
import torch.nn as nn
import pickle5 as pickle
import pandas as pd
import numpy as np
from torch.utils.data import Dataset, DataLoader
from torch.autograd import Variable
from sklearn.ensemble import AdaBoostRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_absolute_error, mean_squared... | 7,495 | 33.703704 | 163 | py |
DMCrypt | DMCrypt-main/model/LSTM.py | import torch
import torch.nn as nn
import pickle
import pandas as pd
import numpy as np
from torch.utils.data import Dataset, DataLoader
from torch.autograd import Variable
from sklearn.preprocessing import MinMaxScaler, StandardScaler
#import seaborn as sns
import matplotlib.pyplot as plt
import pickle5 as pickle
de... | 1,806 | 38.282609 | 97 | py |
paper-log-bilinear-loss | paper-log-bilinear-loss-master/test.py | """
Put it all together with a simple MNIST exmaple
"""
from tensorflow.examples.tutorials.mnist import input_data
from keras.optimizers import Adam
from sklearn.metrics import confusion_matrix
from models import mnist_model
from loss import bilinear_loss
from util import *
DATA_DIR = ""
LRATE = 5e-4 ... | 2,044 | 34.877193 | 119 | py |
paper-log-bilinear-loss | paper-log-bilinear-loss-master/loss.py |
import numpy as np
import tensorflow as tf
from keras import backend as K
def loss_function_generator(conf_mat, log=False, alpha=.5):
"""
Generate Bilinear/Log-Bilinear loss functions combined with the rgular cross-entorpy loss
(1 - alpha)*cross_entropy_loss + alpha*bilinar/log-bilinar
:param conf_m... | 1,997 | 38.176471 | 154 | py |
paper-log-bilinear-loss | paper-log-bilinear-loss-master/models.py |
from keras.layers import Dense, Dropout, Activation, Flatten, Convolution2D, MaxPooling2D
from keras.models import Sequential
def mnist_model():
model = Sequential()
model.add(Convolution2D(20, 5, 5, border_mode='same', activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D(pool_size=(2, 2))... | 2,914 | 41.246377 | 102 | py |
DCAP | DCAP-main/layer.py | import numpy as np
import torch
import torch.nn.functional as F
from torchfm.utils import get_activation_fn
from torchfm.attention_layer import MultiheadAttentionInnerProduct
class FeaturesLinear(torch.nn.Module):
def __init__(self, field_dims, output_dim=1):
super().__init__()
self.fc = torch.nn.... | 12,567 | 36.404762 | 141 | py |
DCAP | DCAP-main/utils.py | import torch.nn.functional as F
import torch
def get_activation_fn(activation: str):
""" Returns the activation function corresponding to `activation` """
if activation == "relu":
return F.relu
# elif activation == "gelu":
# return gelu
# elif activation == "gelu_fast":
# depre... | 736 | 31.043478 | 81 | py |
DCAP | DCAP-main/attention_layer.py | import numpy as np
import torch
import torch.nn.functional as F
from torchfm.utils import get_activation_fn
class MultiheadAttentionInnerProduct(torch.nn.Module):
def __init__(self, num_fields, embed_dim, num_heads, dropout):
super().__init__()
self.num_fields = num_fields
self.mask = (to... | 14,427 | 40.45977 | 171 | py |
DCAP | DCAP-main/dataset/rapid.py | import math
import shutil
import struct
from collections import defaultdict
from functools import lru_cache
from pathlib import Path
import lmdb
import numpy as np
import torch.utils.data
from tqdm import tqdm
class RapidAdvanceDataset(torch.utils.data.Dataset):
"""
MovieLens 1M Dataset
Data preparation... | 1,866 | 27.287879 | 88 | py |
DCAP | DCAP-main/dataset/avazu.py | import shutil
import struct
from collections import defaultdict
from pathlib import Path
import lmdb
import numpy as np
import torch.utils.data
from tqdm import tqdm
class AvazuDataset(torch.utils.data.Dataset):
"""
Avazu Click-Through Rate Prediction Dataset
Dataset preparation
Remove the infre... | 4,268 | 41.267327 | 119 | py |
DCAP | DCAP-main/dataset/frappe.py | import numpy as np
import pandas as pd
import torch.utils.data
class FrappeDataset(torch.utils.data.Dataset):
"""
Frappe Dataset
Data preparation
treat apps with a rating less than 3 as negative samples
:param dataset_path: frappe dataset path
Reference:
https://?
"""
d... | 1,833 | 33.603774 | 144 | py |
DCAP | DCAP-main/dataset/criteo.py | import math
import shutil
import struct
from collections import defaultdict
from functools import lru_cache
from pathlib import Path
import lmdb
import numpy as np
import torch.utils.data
from tqdm import tqdm
class CriteoDataset(torch.utils.data.Dataset):
"""
Criteo Display Advertising Challenge Dataset
... | 5,072 | 41.630252 | 120 | py |
DCAP | DCAP-main/dataset/movielens.py | import numpy as np
import pandas as pd
import torch.utils.data
class MovieLens20MDataset(torch.utils.data.Dataset):
"""
MovieLens 20M Dataset
Data preparation
treat samples with a rating less than 3 as negative samples
:param dataset_path: MovieLens dataset path
Reference:
https... | 2,695 | 32.7 | 103 | py |
DCAP | DCAP-main/model/dcn.py | import torch
from torchfm.layer import FeaturesEmbedding, CrossNetwork, MultiLayerPerceptron
class DeepCrossNetworkModel(torch.nn.Module):
"""
A pytorch implementation of Deep & Cross Network.
Reference:
R Wang, et al. Deep & Cross Network for Ad Click Predictions, 2017.
"""
def __init_... | 1,159 | 35.25 | 101 | py |
DCAP | DCAP-main/model/fnn.py | import torch
from torchfm.layer import FeaturesEmbedding, MultiLayerPerceptron
class FactorizationSupportedNeuralNetworkModel(torch.nn.Module):
"""
A pytorch implementation of Neural Factorization Machine.
Reference:
W Zhang, et al. Deep Learning over Multi-field Categorical Data - A Case Study ... | 924 | 33.259259 | 121 | py |
DCAP | DCAP-main/model/ffm.py | import torch
from torchfm.layer import FeaturesLinear, FieldAwareFactorizationMachine
class FieldAwareFactorizationMachineModel(torch.nn.Module):
"""
A pytorch implementation of Field-aware Factorization Machine.
Reference:
Y Juan, et al. Field-aware Factorization Machines for CTR Prediction, 20... | 809 | 30.153846 | 83 | py |
DCAP | DCAP-main/model/wd.py | import torch
from torchfm.layer import FeaturesLinear, MultiLayerPerceptron, FeaturesEmbedding
class WideAndDeepModel(torch.nn.Module):
"""
A pytorch implementation of wide and deep learning.
Reference:
HT Cheng, et al. Wide & Deep Learning for Recommender Systems, 2016.
"""
def __init_... | 931 | 32.285714 | 81 | py |
DCAP | DCAP-main/model/ncf.py | import torch
from torchfm.layer import FeaturesEmbedding, MultiLayerPerceptron
class NeuralCollaborativeFiltering(torch.nn.Module):
"""
A pytorch implementation of Neural Collaborative Filtering.
Reference:
X He, et al. Neural Collaborative Filtering, 2017.
"""
def __init__(self, field_d... | 1,248 | 35.735294 | 101 | py |
DCAP | DCAP-main/model/dcan.py | import torch
from torchfm.layer import (
FeaturesEmbedding,
FeaturesLinear,
MultiLayerPerceptron
)
from torchfm.attention_layer import CrossAttentionNetwork
class DeepCrossAttentionalNetworkModel(torch.nn.Module):
"""
A pytorch implementation of Multihead Attention Factorization Machine Model.
... | 2,471 | 40.2 | 120 | py |
DCAP | DCAP-main/model/afn.py | import math
import torch
import torch.nn.functional as F
from torchfm.layer import FeaturesEmbedding, FeaturesLinear, MultiLayerPerceptron
class LNN(torch.nn.Module):
"""
A pytorch implementation of LNN layer
Input shape
- A 3D tensor with shape: ``(batch_size,field_size,embedding_size)``.
Out... | 3,088 | 35.341176 | 107 | py |
DCAP | DCAP-main/model/fnfm.py | import torch
from torchfm.layer import FieldAwareFactorizationMachine, MultiLayerPerceptron, FeaturesLinear
class FieldAwareNeuralFactorizationMachineModel(torch.nn.Module):
"""
A pytorch implementation of Field-aware Neural Factorization Machine.
Reference:
L Zhang, et al. Field-aware Neural Fa... | 1,251 | 38.125 | 105 | py |
DCAP | DCAP-main/model/dcap.py | import torch
from torchfm.layer import FeaturesEmbedding, FeaturesLinear, CrossAttentionalProductNetwork, MultiLayerPerceptron
class DeepCrossAttentionalProductNetwork(torch.nn.Module):
"""
A pytorch implementation of inner/outer Product Neural Network.
Reference:
Y Qu, et al. Product-based Neura... | 2,887 | 46.344262 | 113 | py |
DCAP | DCAP-main/model/afi.py | import torch
import torch.nn.functional as F
from torchfm.layer import FeaturesEmbedding, FeaturesLinear, MultiLayerPerceptron
class AutomaticFeatureInteractionModel(torch.nn.Module):
"""
A pytorch implementation of AutoInt.
Reference:
W Song, et al. AutoInt: Automatic Feature Interaction Learni... | 2,157 | 43.040816 | 125 | py |
DCAP | DCAP-main/model/nfm.py | import torch
from torchfm.layer import FactorizationMachine, FeaturesEmbedding, MultiLayerPerceptron, FeaturesLinear
class NeuralFactorizationMachineModel(torch.nn.Module):
"""
A pytorch implementation of Neural Factorization Machine.
Reference:
X He and TS Chua, Neural Factorization Machines fo... | 1,096 | 33.28125 | 103 | py |
DCAP | DCAP-main/model/hofm.py | import torch
from torchfm.layer import FeaturesLinear, FactorizationMachine, AnovaKernel, FeaturesEmbedding
class HighOrderFactorizationMachineModel(torch.nn.Module):
"""
A pytorch implementation of Higher-Order Factorization Machines.
Reference:
M Blondel, et al. Higher-Order Factorization Mach... | 1,473 | 34.095238 | 94 | py |
DCAP | DCAP-main/model/pnn.py | import torch
from torchfm.layer import FeaturesEmbedding, FeaturesLinear, InnerProductNetwork, \
OuterProductNetwork, MultiLayerPerceptron
class ProductNeuralNetworkModel(torch.nn.Module):
"""
A pytorch implementation of inner/outer Product Neural Network.
Reference:
Y Qu, et al. Product-base... | 1,421 | 37.432432 | 118 | py |
DCAP | DCAP-main/model/mhafm.py | import torch
from torchfm.layer import FeaturesEmbedding, FeaturesLinear, MultiLayerPerceptron
from torchfm.attention_layer import CrossAttentionalProductNetwork
class MultiheadAttentionalFactorizationMachineModel(torch.nn.Module):
"""
A pytorch implementation of Multihead Attention Factorization Machine Mod... | 2,488 | 45.092593 | 141 | py |
DCAP | DCAP-main/model/dfm.py | import torch
from torchfm.layer import FactorizationMachine, FeaturesEmbedding, FeaturesLinear, MultiLayerPerceptron
class DeepFactorizationMachineModel(torch.nn.Module):
"""
A pytorch implementation of DeepFM.
Reference:
H Guo, et al. DeepFM: A Factorization-Machine based Neural Network for CTR... | 1,049 | 35.206897 | 103 | py |
DCAP | DCAP-main/model/lr.py | import torch
from torchfm.layer import FeaturesLinear
class LogisticRegressionModel(torch.nn.Module):
"""
A pytorch implementation of Logistic Regression.
"""
def __init__(self, field_dims):
super().__init__()
self.linear = FeaturesLinear(field_dims)
def forward(self, x):
... | 461 | 22.1 | 66 | py |
DCAP | DCAP-main/model/xdfm.py | import torch
from torchfm.layer import CompressedInteractionNetwork, FeaturesEmbedding, FeaturesLinear, MultiLayerPerceptron
class ExtremeDeepFactorizationMachineModel(torch.nn.Module):
"""
A pytorch implementation of xDeepFM.
Reference:
J Lian, et al. xDeepFM: Combining Explicit and Implicit Fe... | 1,157 | 38.931034 | 115 | py |
DCAP | DCAP-main/model/fm.py | import torch
from torchfm.layer import FactorizationMachine, FeaturesEmbedding, FeaturesLinear
class FactorizationMachineModel(torch.nn.Module):
"""
A pytorch implementation of Factorization Machine.
Reference:
S Rendle, Factorization Machines, 2010.
"""
def __init__(self, field_dims, e... | 746 | 27.730769 | 81 | py |
DCAP | DCAP-main/model/afm.py | import torch
from torchfm.layer import FeaturesEmbedding, FeaturesLinear, AttentionalFactorizationMachine
class AttentionalFactorizationMachineModel(torch.nn.Module):
"""
A pytorch implementation of Attentional Factorization Machine.
Reference:
J Xiao, et al. Attentional Factorization Machines: ... | 956 | 34.444444 | 132 | py |
CropRowDetection | CropRowDetection-main/unet-rgbd/dataRGB.py | # -*- coding:utf-8 -*-
from keras.preprocessing.image import img_to_array, load_img
import numpy as np
import glob
class dataProcess(object):
def __init__(self, out_rows, out_cols, data_path="./data/train/image", label_path="./data/train/label",
test_path="./data/test/image", testlabel_path="./d... | 5,060 | 37.340909 | 122 | py |
CropRowDetection | CropRowDetection-main/unet-rgbd/unetRGB.py | # -*- coding:utf-8 -*-
import os
import tensorflow as tf
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
#print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from tensorflow.keras.optimizers import *
from tensorflow.keras.c... | 15,916 | 42.135501 | 201 | py |
CropRowDetection | CropRowDetection-main/unet-rgbd/unetRGBD.py | # -*- coding:utf-8 -*-
import os
import tensorflow as tf
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
#print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from tensorflow.keras.optimizers import *
from tensorflow.keras.c... | 15,916 | 42.135501 | 202 | py |
CropRowDetection | CropRowDetection-main/unet-rgbd/dataRGBD.py | # -*- coding:utf-8 -*-
from keras.preprocessing.image import img_to_array, load_img
import numpy as np
import glob
class dataProcess(object):
def __init__(self, out_rows, out_cols, data_path="./data/train/image", depth_path="./data/train/depth", label_path="./data/train/label",
test_path="./data... | 5,828 | 39.479167 | 158 | py |
Traffic-Benchmark | Traffic-Benchmark-master/train_benchmark.py | import os
import random
import numpy as np
import torch
# import setproctitle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--model',type=str,default='DGCRN',help='model')
parser.add_argument('--data',type=str,default='METR-LA',help='dataset')
args = parser.parse_args()
model = args.model
da... | 6,887 | 46.833333 | 298 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/dcrnn_train_pytorch.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import yaml
from lib.utils import load_graph_data
from model.pytorch.dcrnn_supervisor import DCRNNSupervisor
import setproctitle
setproctitle.setproctitle("stmetanet@lifuxian")
def main(args):... | 1,459 | 38.459459 | 129 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/run_demo_pytorch.py | import argparse
import numpy as np
import os
import sys
import yaml
from lib.utils import load_graph_data
from model.pytorch.dcrnn_supervisor import DCRNNSupervisor
def run_dcrnn(args):
with open(args.config_filename) as f:
supervisor_config = yaml.load(f)
graph_pkl_filename = supervisor_config[... | 1,264 | 36.205882 | 108 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/model/pytorch/dcrnn_model.py | import numpy as np
import torch
import torch.nn as nn
from model.pytorch.dcrnn_cell import DCGRUCell
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
class Seq2SeqAttrs:
def __init__(self... | 29,634 | 40.331939 | 128 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/model/pytorch/dcrnn_cell.py | import numpy as np
import torch
from lib import utils
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class LayerParams:
def __init__(self, rnn_network: torch.nn.Module, layer_type: str):
self._rnn_network = rnn_network
self._params_dict = {}
self._biases_dict = {}
... | 6,939 | 41.576687 | 105 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/model/pytorch/utils.py | import torch
import numpy as np
def masked_mae_loss(y_pred, y_true):
mask = (y_true != 0).float()
mask /= mask.mean()
loss = torch.abs(y_pred - y_true)
loss = loss * mask
# trick for nans: https://discuss.pytorch.org/t/how-to-set-nan-in-tensor-to-0/3918/3
loss[loss != loss] = 0
return loss... | 2,390 | 30.051948 | 88 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/model/pytorch/dcrnn_supervisor.py | import os
import time
import numpy as np
import torch
import torch.nn as nn
# from torch.utils.tensorboard import SummaryWriter
from lib import utils
# from model.pytorch.dcrnn_model import DCRNNModel
from model.pytorch.dcrnn_model import STMetaNet
from model.pytorch.utils import masked_mae_loss, metric, get_normaliz... | 17,117 | 40.853301 | 129 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DCRNN/dcrnn_train_pytorch.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import yaml
from lib.utils import load_graph_data
from model.pytorch.dcrnn_supervisor import DCRNNSupervisor
import setproctitle
setproctitle.setproctitle("dcrnn@lifuxian")
def main(args):
... | 1,455 | 38.351351 | 129 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DCRNN/run_demo_pytorch.py | import argparse
import numpy as np
import os
import sys
import yaml
from lib.utils import load_graph_data
from model.pytorch.dcrnn_supervisor import DCRNNSupervisor
def run_dcrnn(args):
with open(args.config_filename) as f:
supervisor_config = yaml.load(f)
graph_pkl_filename = supervisor_config[... | 1,264 | 36.205882 | 108 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DCRNN/model/pytorch/dcrnn_model.py | import numpy as np
import torch
import torch.nn as nn
from model.pytorch.dcrnn_cell import DCGRUCell
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
class Seq2SeqAttrs:
def __init__(self... | 7,642 | 44.494048 | 119 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DCRNN/model/pytorch/loss.py | import torch
def masked_mae_loss(y_pred, y_true):
mask = (y_true != 0).float()
mask /= mask.mean()
loss = torch.abs(y_pred - y_true)
loss = loss * mask
# trick for nans: https://discuss.pytorch.org/t/how-to-set-nan-in-tensor-to-0/3918/3
loss[loss != loss] = 0
return loss.mean()
| 309 | 24.833333 | 88 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DCRNN/model/pytorch/dcrnn_cell.py | import numpy as np
import torch
from lib import utils
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class LayerParams:
def __init__(self, rnn_network: torch.nn.Module, layer_type: str):
self._rnn_network = rnn_network
self._params_dict = {}
self._biases_dict = {}
... | 6,939 | 41.576687 | 105 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DCRNN/model/pytorch/utils.py | import torch
import numpy as np
def masked_mae_loss(y_pred, y_true):
mask = (y_true != 0).float()
mask /= mask.mean()
loss = torch.abs(y_pred - y_true)
loss = loss * mask
# trick for nans: https://discuss.pytorch.org/t/how-to-set-nan-in-tensor-to-0/3918/3
loss[loss != loss] = 0
return loss... | 2,390 | 30.051948 | 88 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/DCRNN/model/pytorch/dcrnn_supervisor.py | import os
import time
import numpy as np
import torch
# from torch.utils.tensorboard import SummaryWriter
from lib import utils
from model.pytorch.dcrnn_model import DCRNNModel
from model.pytorch.utils import masked_mae_loss, metric, get_normalized_adj
device = torch.device("cuda" if torch.cuda.is_available() else "... | 14,986 | 39.287634 | 129 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/Graph-WaveNet/engine.py | import torch.optim as optim
from model import *
import util
class trainer():
def __init__(self, scaler, in_dim, seq_length, num_nodes, nhid , dropout, lrate, wdecay, device, supports, gcn_bool, addaptadj, aptinit):
self.model = gwnet(device, num_nodes, dropout, supports=supports, gcn_bool=gcn_bool, addaptad... | 1,963 | 43.636364 | 261 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/Graph-WaveNet/test.py | import util
import argparse
from model import *
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
parser = argparse.ArgumentParser()
parser.add_argument('--device',type=str,default='cuda:3',help='')
parser.add_argument('--data',type=str,default='data/METR-LA',help='data path'... | 4,230 | 36.776786 | 142 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/Graph-WaveNet/train_demo.py | import torch
import numpy as np
import argparse
import time
import util
import matplotlib.pyplot as plt
from engine import trainer
parser = argparse.ArgumentParser()
parser.add_argument('--device',type=str,default='cuda:3',help='')
parser.add_argument('--data',type=str,default='data/METR-LA',help='data path')
parser.a... | 9,623 | 37.650602 | 186 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/Graph-WaveNet/model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import sys
class nconv(nn.Module):
def __init__(self):
super(nconv,self).__init__()
def forward(self,x, A):
x = torch.einsum('ncvl,vw->ncwl',(x,A))
return x.contiguous()
class linea... | 7,730 | 35.466981 | 245 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/Graph-WaveNet/util.py | import pickle
import numpy as np
import os
import scipy.sparse as sp
import torch
from scipy.sparse import linalg
class DataLoader(object):
def __init__(self, xs, ys, batch_size, pad_with_last_sample=True):
"""
:param xs:
:param ys:
:param batch_size:
:param pad_with_last_s... | 7,185 | 32.896226 | 113 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/Graph-WaveNet/train.py | import torch
import numpy as np
import argparse
import time
import util
import matplotlib.pyplot as plt
from engine import trainer
parser = argparse.ArgumentParser()
parser.add_argument('--device',type=str,default='cuda:3',help='')
parser.add_argument('--data',type=str,default='data/METR-LA',help='data path')
parser.a... | 8,970 | 38.346491 | 184 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/FNN/dcrnn_train_pytorch.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import yaml
from lib.utils import load_graph_data
from model.pytorch.dcrnn_supervisor import DCRNNSupervisor
import setproctitle
setproctitle.setproctitle("stmetanet@lifuxian")
def main(args):... | 1,459 | 38.459459 | 129 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/FNN/run_demo_pytorch.py | import argparse
import numpy as np
import os
import sys
import yaml
from lib.utils import load_graph_data
from model.pytorch.dcrnn_supervisor import DCRNNSupervisor
def run_dcrnn(args):
with open(args.config_filename) as f:
supervisor_config = yaml.load(f)
graph_pkl_filename = supervisor_config[... | 1,264 | 36.205882 | 108 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/FNN/model/pytorch/dcrnn_model.py | import numpy as np
import torch
import torch.nn as nn
from model.pytorch.dcrnn_cell import DCGRUCell
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
class Seq2SeqAttrs:
def __init__(self... | 30,485 | 40.933975 | 223 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/FNN/model/pytorch/dcrnn_cell.py | import numpy as np
import torch
from lib import utils
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class LayerParams:
def __init__(self, rnn_network: torch.nn.Module, layer_type: str):
self._rnn_network = rnn_network
self._params_dict = {}
self._biases_dict = {}
... | 6,939 | 41.576687 | 105 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/FNN/model/pytorch/utils.py | import torch
import numpy as np
def masked_mae_loss(y_pred, y_true):
mask = (y_true != 0).float()
mask /= mask.mean()
loss = torch.abs(y_pred - y_true)
loss = loss * mask
# trick for nans: https://discuss.pytorch.org/t/how-to-set-nan-in-tensor-to-0/3918/3
loss[loss != loss] = 0
return loss... | 2,390 | 30.051948 | 88 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/FNN/model/pytorch/dcrnn_supervisor.py | import os
import time
import numpy as np
import torch
import torch.nn as nn
# from torch.utils.tensorboard import SummaryWriter
from lib import utils
# from model.pytorch.dcrnn_model import DCRNNModel
from model.pytorch.dcrnn_model import STMetaNet
from model.pytorch.utils import masked_mae_loss, metric, get_normaliz... | 17,117 | 40.853301 | 129 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/MTGNN/layer.py | from __future__ import division
import torch
import torch.nn as nn
from torch.nn import init
import numbers
import torch.nn.functional as F
class nconv(nn.Module):
def __init__(self):
super(nconv,self).__init__()
def forward(self,x, A):
x = torch.einsum('ncvl,vw->ncwl',(x,A))
return x... | 10,549 | 31.164634 | 114 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/MTGNN/train_single_step.py | import argparse
import math
import time
import torch
import torch.nn as nn
from net import gtnet
import numpy as np
import importlib
from util import *
from trainer import Optim
def evaluate(data, X, Y, model, evaluateL2, evaluateL1, batch_size):
model.eval()
total_loss = 0
total_loss_l1 = 0
n_sampl... | 10,199 | 42.220339 | 146 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/MTGNN/net.py | from layer import *
class gtnet(nn.Module):
def __init__(self, gcn_true, buildA_true, gcn_depth, num_nodes, device, predefined_A=None, static_feat=None, dropout=0.3, subgraph_size=20, node_dim=40, dilation_exponential=1, conv_channels=32, residual_channels=32, skip_channels=64, end_channels=128, seq_length=12, in... | 6,760 | 47.292857 | 358 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/MTGNN/util.py | import pickle
import numpy as np
import os
import scipy.sparse as sp
import torch
from scipy.sparse import linalg
from torch.autograd import Variable
def normal_std(x):
return x.std() * np.sqrt((len(x) - 1.)/(len(x)))
class DataLoaderS(object):
# train and valid is the ratio of training set and validation set... | 10,951 | 34.102564 | 116 | py |
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