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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partitioning-with-cliffords | partitioning-with-cliffords-main/data/beh2/beh2_permut/simulations/beh2_wfn_bl_2.8/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/beh2/beh2_permut/simulations/beh2_wfn_bl_1.4/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/beh2/beh2_permut/simulations/beh2_wfn_bl_1.4/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/beh2/beh2_permut/simulations/beh2_wfn_bl_1.4/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/beh2/beh2_permut/simulations/beh2_wfn_bl_2.4/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/beh2/beh2_permut/simulations/beh2_wfn_bl_2.4/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/beh2/beh2_permut/simulations/beh2_wfn_bl_2.4/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/beh2/beh2_permut/simulations/beh2_wfn_bl_1.8/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/beh2/beh2_permut/simulations/beh2_wfn_bl_1.8/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/beh2/beh2_permut/simulations/beh2_wfn_bl_1.8/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/beh2/beh2_permut/simulations/beh2_wfn_bl_2.6/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/beh2/beh2_permut/simulations/beh2_wfn_bl_2.6/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/beh2/beh2_permut/simulations/beh2_wfn_bl_2.6/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/beh2/beh2_permut/tn_update/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/beh2/beh2_permut/tn_update/wfn_optimization.py | import numpy as np
import tequila as tq
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("jax")
import torch
import itertools
import copy
import sys
from my_mpo import *
def normalize(me, order=2):
return me/np.linalg.norm(me, ord=order)
# C... | 14,270 | 37.57027 | 148 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/beh2/beh2_permut/tn_update/qq.py | import numpy as np
import tequila as tq
import tensornetwork as tn
import itertools
import copy
def normalize(me, order=2):
return me/np.linalg.norm(me, ord=order)
# Computes <psiL | H | psiR>
def contract_energy(H, psiL, psiR) -> float:
energy = 0
# For test:
# en_einsum = np.einsum('ijkl, i, j, k,... | 6,227 | 32.483871 | 156 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_1.5/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_1.5/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_1.5/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_1.75/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_1.75/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_1.75/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_0.75/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_0.75/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_0.75/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_1.3/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_1.3/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_1.3/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_2.5/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_2.5/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_2.5/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_1.0/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_1.0/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_1.0/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_3.0/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_3.0/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_3.0/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_2.0/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_2.0/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_2.0/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_2.75/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_2.75/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_2.75/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_2.25/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_2.25/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_2.25/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
mt3 | mt3-main/setup.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 2,153 | 30.676471 | 74 | py |
mt3 | mt3-main/mt3/network.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 13,894 | 32.890244 | 80 | py |
mt3 | mt3-main/mt3/layers.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 32,586 | 38.2142 | 157 | py |
mt3 | mt3-main/mt3/layers_test.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 21,675 | 38.699634 | 81 | py |
FairAC | FairAC-main/src/utils.py | #%%
import numpy as np
import scipy.sparse as sp
import torch
import os
import pandas as pd
import dgl
def encode_onehot(labels):
classes = set(labels)
classes_dict = {c: np.identity(len(classes))[i, :] for i, c in
enumerate(classes)}
labels_onehot = np.array(list(map(classes_dict.get, l... | 8,676 | 33.84739 | 135 | py |
FairAC | FairAC-main/src/train_fairAC_GNN_report.py | import time
import argparse
import dgl
import numpy as np
from sklearn.model_selection import train_test_split
import torch
import torch.nn.functional as F
from utils import accuracy, load_pokec
from models.FairAC import FairAC2, GNN
def parser_args():
# Training settings
parser = argparse.ArgumentParser()
... | 21,680 | 50.376777 | 162 | py |
FairAC | FairAC-main/src/models/HGNN_AC.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class HGNN_AC(nn.Module):
def __init__(self, in_dim, hidden_dim, dropout, activation, num_heads, cuda=False):
super(HGNN_AC, self).__init__()
self.dropout = dropout
self.attentions = [AttentionLayer(in_di... | 2,074 | 38.903846 | 115 | py |
FairAC | FairAC-main/src/models/GCN.py | import torch.nn as nn
import torch.nn.functional as F
from dgl.nn.pytorch import GraphConv
class GCN(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout):
super(GCN, self).__init__()
self.body = GCN_Body(nfeat,nhid,dropout)
self.fc = nn.Linear(nhid,nclass)
def forward(self, g, ... | 830 | 22.742857 | 53 | py |
FairAC | FairAC-main/src/models/FairGNN.py | import random
import torch.nn as nn
from .GCN import GCN,GCN_Body
from .GAT import GAT,GAT_body
from .SAGE import SAGE_Body
from .HGNN_AC import HGNN_AC
import torch
import torch.nn.functional as F
import numpy as np
def get_model(nfeat, args):
if args.model == "GCN":
model = GCN_Body(nfeat,args.num_hidd... | 10,512 | 39.279693 | 159 | py |
FairAC | FairAC-main/src/models/FairAC.py | import random
import torch.nn as nn
from .GCN import GCN,GCN_Body
from .GAT import GAT,GAT_body
from .SAGE import SAGE_Body
from .HGNN_AC import HGNN_AC
import torch
import torch.nn.functional as F
import numpy as np
def get_model(nfeat, args):
if args.model == "GCN":
model = GCN_Body(nfeat,args.num_hidd... | 6,883 | 40.97561 | 131 | py |
FairAC | FairAC-main/src/models/SAGE.py | import torch.nn as nn
import torch.nn.functional as F
from dgl.nn.pytorch import SAGEConv
class SAGE(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout):
super(SAGE, self).__init__()
self.body = SAGE_Body(nfeat,nhid,dropout)
self.fc = nn.Linear(nhid,nclass)
def forward(self, g... | 848 | 23.257143 | 53 | py |
FairAC | FairAC-main/src/models/GAT.py | import torch.nn as nn
import torch.nn.functional as F
from dgl.nn.pytorch import GATConv
class GAT_body(nn.Module):
def __init__(self,
num_layers,
in_dim,
num_hidden,
heads,
feat_drop,
attn_drop,
... | 2,108 | 33.57377 | 115 | py |
Few-shot-WSI | Few-shot-WSI-master/tools/test.py | import argparse
import importlib
import os
import os.path as osp
import time
import mmcv
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
from openselfsup.datasets import build_dataloader, build_dataset
from openselfsup.... | 3,944 | 31.073171 | 83 | py |
Few-shot-WSI | Few-shot-WSI-master/tools/extract.py | import argparse
import importlib
import numpy as np
import os
import os.path as osp
import time
import mmcv
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
from openselfsup.utils import dist_forward_collect, nondist_for... | 6,703 | 35.63388 | 77 | py |
Few-shot-WSI | Few-shot-WSI-master/tools/upgrade_models.py | import torch
import argparse
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument(
'--save-path', type=str, required=True, help='destination file name')
args = parser.parse_args()
return args
def main():
ar... | 712 | 24.464286 | 77 | py |
Few-shot-WSI | Few-shot-WSI-master/tools/extract_backbone_weights.py | import torch
import argparse
def parse_args():
parser = argparse.ArgumentParser(
description='This script extracts backbone weights from a checkpoint')
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument(
'output', type=str, help='destination file name')
args ... | 952 | 28.78125 | 78 | py |
Few-shot-WSI | Few-shot-WSI-master/tools/train.py | from __future__ import division
import argparse
import importlib
import os
import os.path as osp
import time
import mmcv
import torch
from mmcv import Config
from mmcv.runner import init_dist
from openselfsup import __version__
from openselfsup.apis import set_random_seed, train_model
from openselfsup.datasets import... | 5,150 | 33.112583 | 86 | py |
Few-shot-WSI | Few-shot-WSI-master/wsi_workdir/extract.py | import argparse
import importlib
import numpy as np
import os
import os.path as osp
import time
from tqdm import trange,tqdm
import threading
import mmcv
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
from openselfsup... | 3,789 | 29.564516 | 109 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/apis/train.py | import random
import re
from collections import OrderedDict
import numpy as np
import torch
import torch.distributed as dist
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import DistSamplerSeedHook, Runner, obj_from_dict
from openselfsup.datasets import build_dataloader
from ope... | 10,378 | 34.913495 | 87 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/third_party/clustering.py | # This file is modified from
# https://github.com/facebookresearch/deepcluster/blob/master/clustering.py
import time
import numpy as np
import faiss
import torch
from scipy.sparse import csr_matrix
__all__ = ['Kmeans', 'PIC']
def preprocess_features(npdata, pca):
"""Preprocess an array of features.
Args:
... | 9,576 | 29.5 | 84 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/classification.py | import numpy as np
import torch.nn as nn
from openselfsup.utils import print_log
from . import builder
from .registry import MODELS
from .utils import Sobel
@MODELS.register_module
class Classification(nn.Module):
"""Simple image classification.
Args:
backbone (dict): Config dict for module of bac... | 3,370 | 31.413462 | 85 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/simclr.py | import torch
import torch.nn as nn
from openselfsup.utils import print_log
from . import builder
from .registry import MODELS
from .utils import GatherLayer
@MODELS.register_module
class SimCLR(nn.Module):
"""SimCLR.
Implementation of "A Simple Framework for Contrastive Learning
of Visual Representatio... | 3,961 | 35.018182 | 88 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/rotation_pred.py | import torch
import torch.nn as nn
from openselfsup.utils import print_log
from . import builder
from .registry import MODELS
@MODELS.register_module
class RotationPred(nn.Module):
"""Rotation prediction.
Implementation of "Unsupervised Representation Learning
by Predicting Image Rotations (https://arx... | 3,294 | 33.684211 | 79 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/deepcluster.py | import numpy as np
import torch
import torch.nn as nn
from openselfsup.utils import print_log
from . import builder
from .registry import MODELS
from .utils import Sobel
@MODELS.register_module
class DeepCluster(nn.Module):
"""DeepCluster.
Implementation of "Deep Clustering for Unsupervised Learning
o... | 4,526 | 33.557252 | 88 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/relative_loc.py | import torch
import torch.nn as nn
from openselfsup.utils import print_log
from . import builder
from .registry import MODELS
@MODELS.register_module
class RelativeLoc(nn.Module):
"""Relative patch location.
Implementation of "Unsupervised Visual Representation Learning
by Context Prediction (https://a... | 3,948 | 35.564815 | 88 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/moco.py | import torch
import torch.nn as nn
from openselfsup.utils import print_log
from . import builder
from .registry import MODELS
@MODELS.register_module
class MOCO(nn.Module):
"""MOCO.
Implementation of "Momentum Contrast for Unsupervised Visual
Representation Learning (https://arxiv.org/abs/1911.05722)".... | 7,486 | 33.187215 | 88 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/moco_v3.py | import torch
import torch.nn as nn
from openselfsup.utils import print_log
from . import builder
from .registry import MODELS
import torch.nn.functional as F
@MODELS.register_module
class MOCOv3(nn.Module):
def __init__(self,
backbone,
projector=None,
predictor=... | 3,177 | 33.923077 | 77 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/extractor.py | import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import cv2
import math
from sklearn.cluster import KMeans
from openselfsup.utils import print_log
from . import builder
from .registry import MODELS
from .utils import Sobel
### For visualization.
@MODELS.register_module
class Extractor(nn.Mo... | 8,632 | 41.318627 | 121 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/npid.py | import torch
import torch.nn as nn
from openselfsup.utils import print_log
from . import builder
from .registry import MODELS
@MODELS.register_module
class NPID(nn.Module):
"""NPID.
Implementation of "Unsupervised Feature Learning via Non-parametric
Instance Discrimination (https://arxiv.org/abs/1805.0... | 4,658 | 34.564885 | 88 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/byol.py | import torch
import torch.nn as nn
from openselfsup.utils import print_log
from . import builder
from .registry import MODELS
@MODELS.register_module
class BYOL(nn.Module):
"""BYOL.
Implementation of "Bootstrap Your Own Latent: A New Approach to
Self-Supervised Learning (https://arxiv.org/abs/2006.0773... | 4,225 | 36.070175 | 88 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/builder.py | from torch import nn
from openselfsup.utils import build_from_cfg
from .registry import (BACKBONES, MODELS, NECKS, HEADS, MEMORIES, LOSSES)
def build(cfg, registry, default_args=None):
"""Build a module.
Args:
cfg (dict, list[dict]): The config of modules, it is either a dict
or a list o... | 1,274 | 21.368421 | 77 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/odc.py | import numpy as np
import torch
import torch.nn as nn
from openselfsup.utils import print_log
from . import builder
from .registry import MODELS
from .utils import Sobel
@MODELS.register_module
class ODC(nn.Module):
"""ODC.
Official implementation of
"Online Deep Clustering for Unsupervised Representati... | 5,322 | 34.966216 | 88 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/necks.py | import torch
import torch.nn as nn
from packaging import version
from mmcv.cnn import kaiming_init, normal_init
from .registry import NECKS
from .utils import build_norm_layer
def _init_weights(module, init_linear='normal', std=0.01, bias=0.):
assert init_linear in ['normal', 'kaiming'], \
"Undefined ini... | 11,269 | 30.836158 | 85 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/memories/simple_memory.py | import torch
import torch.nn as nn
import torch.distributed as dist
from mmcv.runner import get_dist_info
from openselfsup.utils import AliasMethod
from ..registry import MEMORIES
@MEMORIES.register_module
class SimpleMemory(nn.Module):
"""Simple memory bank for NPID.
Args:
length (int): Number of f... | 2,305 | 33.939394 | 77 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/memories/odc_memory.py | import numpy as np
from sklearn.cluster import KMeans
import torch
import torch.nn as nn
import torch.distributed as dist
from mmcv.runner import get_dist_info
from ..registry import MEMORIES
@MEMORIES.register_module
class ODCMemory(nn.Module):
"""Memory modules for ODC.
Args:
length (int): Number... | 10,441 | 43.623932 | 81 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/utils/multi_pooling.py | import torch.nn as nn
class MultiPooling(nn.Module):
"""Pooling layers for features from multiple depth."""
POOL_PARAMS = {
'resnet50': [
dict(kernel_size=10, stride=10, padding=4),
dict(kernel_size=16, stride=8, padding=0),
dict(kernel_size=13, stride=5, padding=0... | 1,280 | 31.846154 | 66 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/utils/norm.py | import torch.nn as nn
norm_cfg = {
# format: layer_type: (abbreviation, module)
'BN': ('bn', nn.BatchNorm2d),
'SyncBN': ('bn', nn.SyncBatchNorm),
'GN': ('gn', nn.GroupNorm),
# and potentially 'SN'
}
def build_norm_layer(cfg, num_features, postfix=''):
"""Build normalization layer.
Args:
... | 1,684 | 29.089286 | 74 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/utils/scale.py | import torch
import torch.nn as nn
class Scale(nn.Module):
"""A learnable scale parameter."""
def __init__(self, scale=1.0):
super(Scale, self).__init__()
self.scale = nn.Parameter(torch.tensor(scale, dtype=torch.float))
def forward(self, x):
return x * self.scale
| 305 | 20.857143 | 73 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/utils/sobel.py | import torch
import torch.nn as nn
class Sobel(nn.Module):
"""Sobel layer."""
def __init__(self):
super(Sobel, self).__init__()
grayscale = nn.Conv2d(3, 1, kernel_size=1, stride=1, padding=0)
grayscale.weight.data.fill_(1.0 / 3.0)
grayscale.bias.data.zero_()
sobel_filt... | 840 | 32.64 | 74 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/utils/conv_ws.py | import torch.nn as nn
import torch.nn.functional as F
def conv_ws_2d(input,
weight,
bias=None,
stride=1,
padding=0,
dilation=1,
groups=1,
eps=1e-5):
c_in = weight.size(0)
weight_flat = weight.view(c_in, -1... | 1,335 | 27.425532 | 79 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/utils/conv_module.py | import warnings
import torch.nn as nn
from mmcv.cnn import constant_init, kaiming_init
from .conv_ws import ConvWS2d
from .norm import build_norm_layer
conv_cfg = {
'Conv': nn.Conv2d,
'ConvWS': ConvWS2d,
}
def build_conv_layer(cfg, *args, **kwargs):
"""Build convolution layer.
Args:
cfg (N... | 5,723 | 33.902439 | 78 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/utils/accuracy.py | import torch.nn as nn
def accuracy(pred, target, topk=1):
assert isinstance(topk, (int, tuple))
if isinstance(topk, int):
topk = (topk, )
return_single = True
else:
return_single = False
maxk = max(topk)
_, pred_label = pred.topk(maxk, dim=1)
pred_label = pred_label.t(... | 801 | 24.0625 | 69 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/utils/gather_layer.py | import torch
import torch.distributed as dist
class GatherLayer(torch.autograd.Function):
"""Gather tensors from all process, supporting backward propagation.
"""
@staticmethod
def forward(ctx, input):
ctx.save_for_backward(input)
output = [torch.zeros_like(input) \
for _ ... | 618 | 25.913043 | 72 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/backbones/resnet.py | import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import constant_init, kaiming_init
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from openselfsup.utils import get_root_logger
from ..registry import BACKBONES
from ..utils import build_conv_layer, build... | 13,648 | 30.74186 | 79 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/backbones/resnext.py | import math
import torch.nn as nn
from ..registry import BACKBONES
from ..utils import build_conv_layer, build_norm_layer
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNet
class Bottleneck(_Bottleneck):
def __init__(self, inplanes, planes, groups=1, base_width=4, **kwargs):
"""Bo... | 7,594 | 33.058296 | 79 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/heads/contrastive_head.py | import torch
import torch.nn as nn
from ..registry import HEADS
@HEADS.register_module
class ContrastiveHead(nn.Module):
"""Head for contrastive learning.
Args:
temperature (float): The temperature hyper-parameter that
controls the concentration level of the distribution.
Def... | 1,053 | 26.025641 | 65 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/heads/cls_head.py | import torch.nn as nn
from mmcv.cnn import kaiming_init, normal_init
from ..utils import accuracy
from ..registry import HEADS
@HEADS.register_module
class ClsHead(nn.Module):
"""Simplest classifier head, with only one fc layer.
"""
def __init__(self,
with_avg_pool=False,
... | 2,119 | 33.754098 | 78 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/heads/multi_cls_head.py | import torch.nn as nn
from ..utils import accuracy
from ..registry import HEADS
from ..utils import build_norm_layer, MultiPooling
@HEADS.register_module
class MultiClsHead(nn.Module):
"""Multiple classifier heads.
"""
FEAT_CHANNELS = {'resnet50': [64, 256, 512, 1024, 2048]}
FEAT_LAST_UNPOOL = {'res... | 2,682 | 32.962025 | 78 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/models/heads/latent_pred_head.py | import torch
import torch.nn as nn
from mmcv.cnn import normal_init
from ..registry import HEADS
from .. import builder
@HEADS.register_module
class LatentPredictHead(nn.Module):
"""Head for contrastive learning.
"""
def __init__(self, predictor, size_average=True):
super(LatentPredictHead, self)... | 2,048 | 28.695652 | 72 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/datasets/base.py | from abc import ABCMeta, abstractmethod
import torch
from torch.utils.data import Dataset
from openselfsup.utils import print_log, build_from_cfg
from torchvision.transforms import Compose
from .registry import DATASETS, PIPELINES
from .builder import build_datasource
class BaseDataset(Dataset, metaclass=ABCMeta)... | 1,105 | 26.65 | 76 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/datasets/classification.py | import torch
from openselfsup.utils import print_log
from .registry import DATASETS
from .base import BaseDataset
from .utils import to_numpy
@DATASETS.register_module
class ClassificationDataset(BaseDataset):
"""Dataset for classification.
"""
def __init__(self, data_source, pipeline, prefetch=False):... | 1,565 | 33.8 | 84 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/datasets/rotation_pred.py | import torch
from PIL import Image
from .registry import DATASETS
from .base import BaseDataset
def rotate(img):
"""Rotate input image with 0, 90, 180, and 270 degrees.
Args:
img (Tensor): input image of shape (C, H, W).
Returns:
list[Tensor]: A list of four rotated images.
"""
... | 1,288 | 27.021739 | 78 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/datasets/relative_loc.py | from openselfsup.utils import build_from_cfg
import torch
from PIL import Image
from torchvision.transforms import Compose, RandomCrop
import torchvision.transforms.functional as TF
from .registry import DATASETS, PIPELINES
from .base import BaseDataset
def image_to_patches(img):
"""Crop split_per_side x split_... | 2,327 | 34.272727 | 94 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/datasets/dataset_wrappers.py | import numpy as np
from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
from .registry import DATASETS
@DATASETS.register_module
class ConcatDataset(_ConcatDataset):
"""A wrapper of concatenated dataset.
Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but
concat the group flag for... | 1,639 | 28.285714 | 78 | py |
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