index int64 0 50.7k | project_path stringclasses 279
values | completion_path stringlengths 57 199 | signature_postion stringlengths 3 11 | class_position stringlengths 3 11 | function_name stringlengths 1 62 | class_name stringlengths 0 55 | body_position stringlengths 3 11 | body_position_with_comments stringlengths 3 11 | masked_class stringlengths 14 8.32k | masked_class_with_comment stringlengths 14 8.32k | func_body stringlengths 9 6.72k | func_body_with_comment stringlengths 9 6.72k | len_func_body int64 1 643 | len_input int64 10 2.02k | len_output int64 4 1.9k | total int64 20 2.05k | file_code stringlengths 14 442k | file_code_with_comment stringlengths 14 442k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
50,397 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/coerce.py | 6,8 | 1,80 | symbol | Coerce | 8,24 | 8,24 | class Coerce(object):
@staticmethod
def bool(space, w_obj):
return space.is_true(w_obj)
@staticmethod
def symbol(space, w_obj):
FILL_FUNC_BODY @staticmethod
def int(space, w_obj):
if space.is_kind_of(w_obj, space.w_fixnum):
return space.int_w(w_obj)
else:
... | class Coerce(object):
@staticmethod
def bool(space, w_obj):
return space.is_true(w_obj)
@staticmethod
def symbol(space, w_obj):
FILL_FUNC_BODY @staticmethod
def int(space, w_obj):
if space.is_kind_of(w_obj, space.w_fixnum):
return space.int_w(w_obj)
else:
... | if space.is_kind_of(w_obj, space.w_symbol):
return space.symbol_w(w_obj)
else:
w_str = space.convert_type(w_obj, space.w_string, "to_str",
raise_error=False)
if w_str is space.w_nil:
w_inspect_str = space.send(w_o... | if space.is_kind_of(w_obj, space.w_symbol):
return space.symbol_w(w_obj)
else:
w_str = space.convert_type(w_obj, space.w_string, "to_str",
raise_error=False)
if w_str is space.w_nil:
w_inspect_str = space.send(w_o... | 43 | 747 | 235 | 982 | class Coerce(object):
@staticmethod
def bool(space, w_obj):
return space.is_true(w_obj)
@staticmethod
def symbol(space, w_obj):
FILL_FUNC_BODY @staticmethod
def int(space, w_obj):
if space.is_kind_of(w_obj, space.w_fixnum):
return space.int_w(w_obj)
else:
... | class Coerce(object):
@staticmethod
def bool(space, w_obj):
return space.is_true(w_obj)
@staticmethod
def symbol(space, w_obj):
FILL_FUNC_BODY @staticmethod
def int(space, w_obj):
if space.is_kind_of(w_obj, space.w_fixnum):
return space.int_w(w_obj)
else:
... |
50,398 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/error.py | 44,45 | 1,61 | error_for_oserror | 45,48 | 45,48 | import os
import errno
class RubyError(Exception):
def __init__(self, w_value):
self.w_value = w_value
def __str__(self):
return "<RubyError: %s>" % self.w_value
def format_traceback(space, exc, top_filepath):
w_bt = space.send(exc, "backtrace")
bt_w = space.listview(w_bt)
if bt... | import os
import errno
class RubyError(Exception):
def __init__(self, w_value):
self.w_value = w_value
def __str__(self):
return "<RubyError: %s>" % self.w_value
def format_traceback(space, exc, top_filepath):
w_bt = space.send(exc, "backtrace")
bt_w = space.listview(w_bt)
if bt... | return error_for_errno(space, exc.errno)
| return error_for_errno(space, exc.errno)
| 3 | 606 | 22 | 628 | import os
import errno
class RubyError(Exception):
def __init__(self, w_value):
self.w_value = w_value
def __str__(self):
return "<RubyError: %s>" % self.w_value
def format_traceback(space, exc, top_filepath):
w_bt = space.send(exc, "backtrace")
bt_w = space.listview(w_bt)
if bt... | import os
import errno
class RubyError(Exception):
def __init__(self, w_value):
self.w_value = w_value
def __str__(self):
return "<RubyError: %s>" % self.w_value
def format_traceback(space, exc, top_filepath):
w_bt = space.send(exc, "backtrace")
bt_w = space.listview(w_bt)
if bt... | |
50,399 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/module.py | 103,104 | 1,180 | check_frozen | 104,124 | 104,124 | import functools
from topaz.gateway import WrapperGenerator
from topaz.utils.cache import Cache
class ClassDef(object):
def __init__(self, name, superclassdef=None):
self.name = name
self.methods = {}
self.singleton_methods = {}
self.includes = []
self.setup_class_func = N... | import functools
from topaz.gateway import WrapperGenerator
from topaz.utils.cache import Cache
class ClassDef(object):
def __init__(self, name, superclassdef=None):
self.name = name
self.methods = {}
self.singleton_methods = {}
self.includes = []
self.setup_class_func = N... | def inner(func):
code = func.__code__
space_idx = code.co_varnames.index("space")
obj_idx = code.co_varnames.index(param)
@functools.wraps(func)
def wrapper(*args):
space = args[space_idx]
w_obj = args[obj_idx]
if space.is_true(w_obj.get_f... | def inner(func):
code = func.__code__
space_idx = code.co_varnames.index("space")
obj_idx = code.co_varnames.index(param)
@functools.wraps(func)
def wrapper(*args):
space = args[space_idx]
w_obj = args[obj_idx]
if space.is_true(w_obj.get_f... | 44 | 1,490 | 212 | 1,702 | import functools
from topaz.gateway import WrapperGenerator
from topaz.utils.cache import Cache
class ClassDef(object):
def __init__(self, name, superclassdef=None):
self.name = name
self.methods = {}
self.singleton_methods = {}
self.includes = []
self.setup_class_func = N... | import functools
from topaz.gateway import WrapperGenerator
from topaz.utils.cache import Cache
class ClassDef(object):
def __init__(self, name, superclassdef=None):
self.name = name
self.methods = {}
self.singleton_methods = {}
self.includes = []
self.setup_class_func = N... | |
50,400 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/module.py | 125,126 | 124,158 | _build | ClassCache | 126,158 | 126,158 | class ClassCache(Cache):
def _build(self, classdef):
FILL_FUNC_BODY | class ClassCache(Cache):
def _build(self, classdef):
FILL_FUNC_BODY | from topaz.objects.classobject import W_ClassObject
from topaz.objects.functionobject import W_BuiltinFunction
assert classdef.cls is not None, classdef.name
if classdef.superclassdef is None:
superclass = None
else:
superclass = self.space.getclassobjec... | from topaz.objects.classobject import W_ClassObject
from topaz.objects.functionobject import W_BuiltinFunction
assert classdef.cls is not None, classdef.name
if classdef.superclassdef is None:
superclass = None
else:
superclass = self.space.getclassobjec... | 87 | 28 | 412 | 440 | import functools
from topaz.gateway import WrapperGenerator
from topaz.utils.cache import Cache
class ClassDef(object):
def __init__(self, name, superclassdef=None):
self.name = name
self.methods = {}
self.singleton_methods = {}
self.includes = []
self.setup_class_func = N... | import functools
from topaz.gateway import WrapperGenerator
from topaz.utils.cache import Cache
class ClassDef(object):
def __init__(self, name, superclassdef=None):
self.name = name
self.methods = {}
self.singleton_methods = {}
self.includes = []
self.setup_class_func = N... |
50,401 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/module.py | 37,38 | 7,69 | singleton_method | ClassDef | 38,47 | 38,47 | class ClassDef(object):
def __init__(self, name, superclassdef=None):
self.name = name
self.methods = {}
self.singleton_methods = {}
self.includes = []
self.setup_class_func = None
self.superclassdef = superclassdef
self.cls = None
def __repr__(self):
... | class ClassDef(object):
def __init__(self, name, superclassdef=None):
self.name = name
self.methods = {}
self.singleton_methods = {}
self.includes = []
self.setup_class_func = None
self.superclassdef = superclassdef
self.cls = None
def __repr__(self):
... | name = __name
def adder(func):
if isinstance(func, staticmethod):
func = func.__func__
self.singleton_methods[name] = (func, argspec)
return staticmethod(func)
return adder
| name = __name
def adder(func):
if isinstance(func, staticmethod):
func = func.__func__
self.singleton_methods[name] = (func, argspec)
return staticmethod(func)
return adder
| 19 | 448 | 75 | 523 | import functools
from topaz.gateway import WrapperGenerator
from topaz.utils.cache import Cache
class ClassDef(object):
def __init__(self, name, superclassdef=None):
self.name = name
self.methods = {}
self.singleton_methods = {}
self.includes = []
self.setup_class_func = N... | import functools
from topaz.gateway import WrapperGenerator
from topaz.utils.cache import Cache
class ClassDef(object):
def __init__(self, name, superclassdef=None):
self.name = name
self.methods = {}
self.singleton_methods = {}
self.includes = []
self.setup_class_func = N... |
50,402 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/module.py | 159,160 | 158,180 | _build | ModuleCache | 160,180 | 160,180 | class ModuleCache(Cache):
def _build(self, moduledef):
FILL_FUNC_BODY | class ModuleCache(Cache):
def _build(self, moduledef):
FILL_FUNC_BODY | from topaz.objects.functionobject import W_BuiltinFunction
from topaz.objects.moduleobject import W_ModuleObject
from topaz.objects.objectobject import W_BaseObject
w_mod = self.space.newmodule(moduledef.name)
for name, (method, argspec) in moduledef.methods.iteritems():
... | from topaz.objects.functionobject import W_BuiltinFunction
from topaz.objects.moduleobject import W_ModuleObject
from topaz.objects.objectobject import W_BaseObject
w_mod = self.space.newmodule(moduledef.name)
for name, (method, argspec) in moduledef.methods.iteritems():
... | 62 | 29 | 297 | 326 | import functools
from topaz.gateway import WrapperGenerator
from topaz.utils.cache import Cache
class ClassDef(object):
def __init__(self, name, superclassdef=None):
self.name = name
self.methods = {}
self.singleton_methods = {}
self.includes = []
self.setup_class_func = N... | import functools
from topaz.gateway import WrapperGenerator
from topaz.utils.cache import Cache
class ClassDef(object):
def __init__(self, name, superclassdef=None):
self.name = name
self.methods = {}
self.singleton_methods = {}
self.includes = []
self.setup_class_func = N... |
50,403 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/module.py | 91,92 | 69,103 | adder | ModuleDef | 92,96 | 92,96 | class ModuleDef(object):
def __init__(self, name):
self.name = name
self.methods = {}
self.singleton_methods = {}
self.setup_module_func = None
def __deepcopy__(self, memo):
return self
def method(self, __name, **argspec):
name = __name
def adder(f... | class ModuleDef(object):
def __init__(self, name):
self.name = name
self.methods = {}
self.singleton_methods = {}
self.setup_module_func = None
def __deepcopy__(self, memo):
return self
def method(self, __name, **argspec):
name = __name
def adder(f... | # TODO: should be private, once we have visibility
self.methods[name] = (func, argspec)
self.singleton_methods[name] = (func, argspec)
return func
| # TODO: should be private, once we have visibility
self.methods[name] = (func, argspec)
self.singleton_methods[name] = (func, argspec)
return func
| 19 | 198 | 54 | 252 | import functools
from topaz.gateway import WrapperGenerator
from topaz.utils.cache import Cache
class ClassDef(object):
def __init__(self, name, superclassdef=None):
self.name = name
self.methods = {}
self.singleton_methods = {}
self.includes = []
self.setup_class_func = N... | import functools
from topaz.gateway import WrapperGenerator
from topaz.utils.cache import Cache
class ClassDef(object):
def __init__(self, name, superclassdef=None):
self.name = name
self.methods = {}
self.singleton_methods = {}
self.includes = []
self.setup_class_func = N... |
50,404 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/modules/ffi/data_converter.py | 10,12 | 5,33 | native_type | DataConverter | 12,26 | 12,26 | class DataConverter(object):
# A rather abstract base class
moduledef = ModuleDef('FFI::DataConverter')
@moduledef.method('native_type')
def native_type(self, space, args_w):
FILL_FUNC_BODY @moduledef.method('to_native')
def to_native(self, space, w_value, w_ctx):
return w_value
@m... | class DataConverter(object):
# A rather abstract base class
moduledef = ModuleDef('FFI::DataConverter')
@moduledef.method('native_type')
def native_type(self, space, args_w):
FILL_FUNC_BODY @moduledef.method('to_native')
def to_native(self, space, w_value, w_ctx):
return w_value
@m... | # if len(args_w) == 0:
# raise space.error(space.w_NotImplementedError,
# "native_type method not overridden and no "
# "native_type set")
if len(args_w) == 0:
w_void = space.execute("FFI::Type::VOID")
space.set... | # if len(args_w) == 0:
# raise space.error(space.w_NotImplementedError,
# "native_type method not overridden and no "
# "native_type set")
if len(args_w) == 0:
w_void = space.execute("FFI::Type::VOID")
space.set... | 48 | 147 | 221 | 368 | from topaz.module import ModuleDef
from topaz.modules.ffi.type import type_object
class DataConverter(object):
# A rather abstract base class
moduledef = ModuleDef('FFI::DataConverter')
@moduledef.method('native_type')
def native_type(self, space, args_w):
FILL_FUNC_BODY @moduledef.method('to_nat... | from topaz.module import ModuleDef
from topaz.modules.ffi.type import type_object
class DataConverter(object):
# A rather abstract base class
moduledef = ModuleDef('FFI::DataConverter')
@moduledef.method('native_type')
def native_type(self, space, args_w):
FILL_FUNC_BODY @moduledef.method('to_nat... |
50,405 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/objects/bindingobject.py | 19,21 | 6,30 | method_eval | W_BindingObject | 21,30 | 21,30 | class W_BindingObject(W_Object):
classdef = ClassDef("Binding", W_Object.classdef)
_immutable_fields_ = ["names[*]", "cells[*]", "w_self", "lexical_scope"]
def __init__(self, space, names, cells, w_self, lexical_scope):
W_Object.__init__(self, space)
self.names = names
self.cells = ... | class W_BindingObject(W_Object):
classdef = ClassDef("Binding", W_Object.classdef)
_immutable_fields_ = ["names[*]", "cells[*]", "w_self", "lexical_scope"]
def __init__(self, space, names, cells, w_self, lexical_scope):
W_Object.__init__(self, space)
self.names = names
self.cells = ... | symtable = SymbolTable()
for name in self.names:
symtable.cells[name] = symtable.FREEVAR
bc = space.compile(source, "(eval)", symtable=symtable)
frame = space.create_frame(bc, w_self=self.w_self, lexical_scope=self.lexical_scope)
for idx, cell in enumerate(self.cells)... | symtable = SymbolTable()
for name in self.names:
symtable.cells[name] = symtable.FREEVAR
bc = space.compile(source, "(eval)", symtable=symtable)
frame = space.create_frame(bc, w_self=self.w_self, lexical_scope=self.lexical_scope)
for idx, cell in enumerate(self.cells)... | 35 | 189 | 162 | 351 | from topaz.astcompiler import SymbolTable
from topaz.module import ClassDef
from topaz.objects.objectobject import W_Object
class W_BindingObject(W_Object):
classdef = ClassDef("Binding", W_Object.classdef)
_immutable_fields_ = ["names[*]", "cells[*]", "w_self", "lexical_scope"]
def __init__(self, space,... | from topaz.astcompiler import SymbolTable
from topaz.module import ClassDef
from topaz.objects.objectobject import W_Object
class W_BindingObject(W_Object):
classdef = ClassDef("Binding", W_Object.classdef)
_immutable_fields_ = ["names[*]", "cells[*]", "w_self", "lexical_scope"]
def __init__(self, space,... |
50,406 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/objects/boolobject.py | 55,57 | 33,62 | method_or | W_FalseObject | 57,59 | 57,59 | class W_FalseObject(W_Object):
classdef = ClassDef("FalseClass", W_Object.classdef)
def is_true(self, space):
return False
def getsingletonclass(self, space):
return space.getclassfor(W_FalseObject)
@classdef.method("inspect")
@classdef.method("to_s")
def method_to_s(self, spa... | class W_FalseObject(W_Object):
classdef = ClassDef("FalseClass", W_Object.classdef)
def is_true(self, space):
return False
def getsingletonclass(self, space):
return space.getclassfor(W_FalseObject)
@classdef.method("inspect")
@classdef.method("to_s")
def method_to_s(self, spa... | return space.newbool(space.is_true(w_other))
| return space.newbool(space.is_true(w_other))
| 2 | 277 | 21 | 298 | from topaz.module import ClassDef
from topaz.objects.objectobject import W_Object
class W_TrueObject(W_Object):
classdef = ClassDef("TrueClass", W_Object.classdef)
def getsingletonclass(self, space):
return space.getclassfor(W_TrueObject)
@classdef.method("inspect")
@classdef.method("to_s")
... | from topaz.module import ClassDef
from topaz.objects.objectobject import W_Object
class W_TrueObject(W_Object):
classdef = ClassDef("TrueClass", W_Object.classdef)
def getsingletonclass(self, space):
return space.getclassfor(W_TrueObject)
@classdef.method("inspect")
@classdef.method("to_s")
... |
50,407 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/objects/boolobject.py | 8,9 | 5,33 | getsingletonclass | W_TrueObject | 9,11 | 9,11 | class W_TrueObject(W_Object):
classdef = ClassDef("TrueClass", W_Object.classdef)
def getsingletonclass(self, space):
FILL_FUNC_BODY @classdef.method("inspect")
@classdef.method("to_s")
def method_to_s(self, space):
return space.newstr_fromstr("true")
@classdef.method("==")
def meth... | class W_TrueObject(W_Object):
classdef = ClassDef("TrueClass", W_Object.classdef)
def getsingletonclass(self, space):
FILL_FUNC_BODY @classdef.method("inspect")
@classdef.method("to_s")
def method_to_s(self, space):
return space.newstr_fromstr("true")
@classdef.method("==")
def meth... | return space.getclassfor(W_TrueObject)
| return space.getclassfor(W_TrueObject)
| 2 | 268 | 17 | 285 | from topaz.module import ClassDef
from topaz.objects.objectobject import W_Object
class W_TrueObject(W_Object):
classdef = ClassDef("TrueClass", W_Object.classdef)
def getsingletonclass(self, space):
FILL_FUNC_BODY @classdef.method("inspect")
@classdef.method("to_s")
def method_to_s(self, space):
... | from topaz.module import ClassDef
from topaz.objects.objectobject import W_Object
class W_TrueObject(W_Object):
classdef = ClassDef("TrueClass", W_Object.classdef)
def getsingletonclass(self, space):
FILL_FUNC_BODY @classdef.method("inspect")
@classdef.method("to_s")
def method_to_s(self, space):
... |
50,408 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/objects/codeobject.py | 82,83 | 7,91 | arity | W_CodeObject | 83,88 | 83,88 | class W_CodeObject(W_BaseObject):
_immutable_fields_ = [
"code", "consts_w[*]", "max_stackdepth", "cellvars[*]", "freevars[*]",
"arg_pos[*]", "defaults[*]", "block_arg_pos", "splat_arg_pos",
"kwarg_names[*]", "kwrest_pos", "kw_defaults[*]", "default_arg_begin",
"filepath", "lineno",
... | class W_CodeObject(W_BaseObject):
_immutable_fields_ = [
"code", "consts_w[*]", "max_stackdepth", "cellvars[*]", "freevars[*]",
"arg_pos[*]", "defaults[*]", "block_arg_pos", "splat_arg_pos",
"kwarg_names[*]", "kwrest_pos", "kw_defaults[*]", "default_arg_begin",
"filepath", "lineno",
... | args_count = len(self.arg_pos) - len(self.defaults)
if self.splat_arg_pos != -1 or (negative_defaults and len(self.defaults) > 0):
args_count = -(args_count + 1)
return args_count
| args_count = len(self.arg_pos) - len(self.defaults)
if self.splat_arg_pos != -1 or (negative_defaults and len(self.defaults) > 0):
args_count = -(args_count + 1)
return args_count
| 22 | 974 | 77 | 1,051 | import copy
from topaz.module import ClassDef
from topaz.objects.objectobject import W_BaseObject
class W_CodeObject(W_BaseObject):
_immutable_fields_ = [
"code", "consts_w[*]", "max_stackdepth", "cellvars[*]", "freevars[*]",
"arg_pos[*]", "defaults[*]", "block_arg_pos", "splat_arg_pos",
... | import copy
from topaz.module import ClassDef
from topaz.objects.objectobject import W_BaseObject
class W_CodeObject(W_BaseObject):
_immutable_fields_ = [
"code", "consts_w[*]", "max_stackdepth", "cellvars[*]", "freevars[*]",
"arg_pos[*]", "defaults[*]", "block_arg_pos", "splat_arg_pos",
... |
50,409 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/objects/encodingobject.py | 24,26 | 5,31 | singleton_method_aliases | W_EncodingObject | 26,28 | 26,28 | class W_EncodingObject(W_Object):
classdef = ClassDef("Encoding", W_Object.classdef)
@classdef.singleton_method("default_external")
def singleton_method_default_external(self, space):
return W_EncodingObject(space)
@classdef.singleton_method("default_internal")
def singleton_method_default... | class W_EncodingObject(W_Object):
classdef = ClassDef("Encoding", W_Object.classdef)
@classdef.singleton_method("default_external")
def singleton_method_default_external(self, space):
return W_EncodingObject(space)
@classdef.singleton_method("default_internal")
def singleton_method_default... | return space.newarray([])
| return space.newarray([])
| 2 | 277 | 13 | 290 | from topaz.module import ClassDef
from topaz.objects.objectobject import W_Object
class W_EncodingObject(W_Object):
classdef = ClassDef("Encoding", W_Object.classdef)
@classdef.singleton_method("default_external")
def singleton_method_default_external(self, space):
return W_EncodingObject(space)
... | from topaz.module import ClassDef
from topaz.objects.objectobject import W_Object
class W_EncodingObject(W_Object):
classdef = ClassDef("Encoding", W_Object.classdef)
@classdef.singleton_method("default_external")
def singleton_method_default_external(self, space):
return W_EncodingObject(space)
... |
50,410 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/objects/functionobject.py | 83,84 | 71,93 | call | W_BuiltinFunction | 84,93 | 84,93 | class W_BuiltinFunction(W_FunctionObject):
_immutable_fields_ = ["func"]
def __init__(self, name, w_class, func, visibility=W_FunctionObject.PUBLIC):
W_FunctionObject.__init__(self, name, w_class, visibility=visibility)
self.func = func
def __deepcopy__(self, memo):
obj = super(W_B... | class W_BuiltinFunction(W_FunctionObject):
_immutable_fields_ = ["func"]
def __init__(self, name, w_class, func, visibility=W_FunctionObject.PUBLIC):
W_FunctionObject.__init__(self, name, w_class, visibility=visibility)
self.func = func
def __deepcopy__(self, memo):
obj = super(W_B... | from topaz.frame import BuiltinFrame
frame = BuiltinFrame(self.name)
ec = space.getexecutioncontext()
ec.invoke_trace_proc(space, "c-call", self.name, self.w_class.name)
with ec.visit_frame(frame):
w_res = self.func(w_receiver, space, args_w, block)
ec.invoke... | from topaz.frame import BuiltinFrame
frame = BuiltinFrame(self.name)
ec = space.getexecutioncontext()
ec.invoke_trace_proc(space, "c-call", self.name, self.w_class.name)
with ec.visit_frame(frame):
w_res = self.func(w_receiver, space, args_w, block)
ec.invoke... | 28 | 162 | 144 | 306 | import copy
from topaz.objects.objectobject import W_BaseObject
class W_FunctionObject(W_BaseObject):
_immutable_fields_ = ["name", "w_class", "visibility?"]
PUBLIC = 0
PROTECTED = 1
PRIVATE = 2
MODULE_FUNCTION = 3
def __init__(self, name, w_class=None, visibility=PUBLIC):
self.name... | import copy
from topaz.objects.objectobject import W_BaseObject
class W_FunctionObject(W_BaseObject):
_immutable_fields_ = ["name", "w_class", "visibility?"]
PUBLIC = 0
PROTECTED = 1
PRIVATE = 2
MODULE_FUNCTION = 3
def __init__(self, name, w_class=None, visibility=PUBLIC):
self.name... |
50,411 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/objects/functionobject.py | 26,27 | 6,36 | update_visibility | W_FunctionObject | 27,29 | 27,29 | class W_FunctionObject(W_BaseObject):
_immutable_fields_ = ["name", "w_class", "visibility?"]
PUBLIC = 0
PROTECTED = 1
PRIVATE = 2
MODULE_FUNCTION = 3
def __init__(self, name, w_class=None, visibility=PUBLIC):
self.name = name
self.w_class = w_class
self.visibility = vi... | class W_FunctionObject(W_BaseObject):
_immutable_fields_ = ["name", "w_class", "visibility?"]
PUBLIC = 0
PROTECTED = 1
PRIVATE = 2
MODULE_FUNCTION = 3
def __init__(self, name, w_class=None, visibility=PUBLIC):
self.name = name
self.w_class = w_class
self.visibility = vi... | self.visibility = visibility
| self.visibility = visibility
| 3 | 268 | 11 | 279 | import copy
from topaz.objects.objectobject import W_BaseObject
class W_FunctionObject(W_BaseObject):
_immutable_fields_ = ["name", "w_class", "visibility?"]
PUBLIC = 0
PROTECTED = 1
PRIVATE = 2
MODULE_FUNCTION = 3
def __init__(self, name, w_class=None, visibility=PUBLIC):
self.name... | import copy
from topaz.objects.objectobject import W_BaseObject
class W_FunctionObject(W_BaseObject):
_immutable_fields_ = ["name", "w_class", "visibility?"]
PUBLIC = 0
PROTECTED = 1
PRIVATE = 2
MODULE_FUNCTION = 3
def __init__(self, name, w_class=None, visibility=PUBLIC):
self.name... |
50,412 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/objects/functionobject.py | 50,51 | 36,71 | call | W_UserFunction | 51,61 | 51,61 | class W_UserFunction(W_FunctionObject):
_immutable_fields_ = ["bytecode", "lexical_scope"]
def __init__(self, name, bytecode, lexical_scope, visibility=W_FunctionObject.PUBLIC):
W_FunctionObject.__init__(self, name, visibility=visibility)
self.bytecode = bytecode
self.lexical_scope = le... | class W_UserFunction(W_FunctionObject):
_immutable_fields_ = ["bytecode", "lexical_scope"]
def __init__(self, name, bytecode, lexical_scope, visibility=W_FunctionObject.PUBLIC):
W_FunctionObject.__init__(self, name, visibility=visibility)
self.bytecode = bytecode
self.lexical_scope = le... | frame = space.create_frame(
self.bytecode,
w_self=w_receiver,
lexical_scope=self.lexical_scope,
block=block,
)
with space.getexecutioncontext().visit_frame(frame):
frame.handle_args(space, self.bytecode, args_w, block)
retur... | frame = space.create_frame(
self.bytecode,
w_self=w_receiver,
lexical_scope=self.lexical_scope,
block=block,
)
with space.getexecutioncontext().visit_frame(frame):
frame.handle_args(space, self.bytecode, args_w, block)
retur... | 17 | 305 | 116 | 421 | import copy
from topaz.objects.objectobject import W_BaseObject
class W_FunctionObject(W_BaseObject):
_immutable_fields_ = ["name", "w_class", "visibility?"]
PUBLIC = 0
PROTECTED = 1
PRIVATE = 2
MODULE_FUNCTION = 3
def __init__(self, name, w_class=None, visibility=PUBLIC):
self.name... | import copy
from topaz.objects.objectobject import W_BaseObject
class W_FunctionObject(W_BaseObject):
_immutable_fields_ = ["name", "w_class", "visibility?"]
PUBLIC = 0
PROTECTED = 1
PRIVATE = 2
MODULE_FUNCTION = 3
def __init__(self, name, w_class=None, visibility=PUBLIC):
self.name... |
50,413 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/objects/nilobject.py | 18,20 | 5,46 | method_to_s | W_NilObject | 20,22 | 20,22 | class W_NilObject(W_Object):
classdef = ClassDef("NilClass", W_Object.classdef)
def is_true(self, space):
return False
def getsingletonclass(self, space):
return space.getclassfor(W_NilObject)
@classdef.method("nil?")
def method_nilp(self, space):
return space.w_true
... | class W_NilObject(W_Object):
classdef = ClassDef("NilClass", W_Object.classdef)
def is_true(self, space):
return False
def getsingletonclass(self, space):
return space.getclassfor(W_NilObject)
@classdef.method("nil?")
def method_nilp(self, space):
return space.w_true
... | return space.newstr_fromstr("")
| return space.newstr_fromstr("")
| 2 | 377 | 15 | 392 | from topaz.module import ClassDef
from topaz.objects.objectobject import W_Object
class W_NilObject(W_Object):
classdef = ClassDef("NilClass", W_Object.classdef)
def is_true(self, space):
return False
def getsingletonclass(self, space):
return space.getclassfor(W_NilObject)
@classde... | from topaz.module import ClassDef
from topaz.objects.objectobject import W_Object
class W_NilObject(W_Object):
classdef = ClassDef("NilClass", W_Object.classdef)
def is_true(self, space):
return False
def getsingletonclass(self, space):
return space.getclassfor(W_NilObject)
@classde... |
50,414 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/objects/procobject.py | 46,51 | 5,84 | method_call | W_ProcObject | 51,66 | 51,66 | class W_ProcObject(W_Object):
classdef = ClassDef("Proc", W_Object.classdef)
_immutable_fields_ = [
"bytecode", "w_self", "lexical_scope", "cells[*]",
"block", "parent_interp", "top_parent_interp",
"regexp_match_cell", "is_lambda"]
def __init__(self, space, bytecode, w_self, lexical... | class W_ProcObject(W_Object):
classdef = ClassDef("Proc", W_Object.classdef)
_immutable_fields_ = [
"bytecode", "w_self", "lexical_scope", "cells[*]",
"block", "parent_interp", "top_parent_interp",
"regexp_match_cell", "is_lambda"]
def __init__(self, space, bytecode, w_self, lexical... | from topaz.interpreter import RaiseReturn, RaiseBreak
try:
return space.invoke_block(self, args_w, block_arg=block)
except RaiseReturn as e:
if self.is_lambda:
return e.w_value
else:
raise
except RaiseBreak as e:
... | from topaz.interpreter import RaiseReturn, RaiseBreak
try:
return space.invoke_block(self, args_w, block_arg=block)
except RaiseReturn as e:
if self.is_lambda:
return e.w_value
else:
raise
except RaiseBreak as e:
... | 34 | 766 | 145 | 911 | from topaz.module import ClassDef
from topaz.objects.objectobject import W_Object
class W_ProcObject(W_Object):
classdef = ClassDef("Proc", W_Object.classdef)
_immutable_fields_ = [
"bytecode", "w_self", "lexical_scope", "cells[*]",
"block", "parent_interp", "top_parent_interp",
"regex... | from topaz.module import ClassDef
from topaz.objects.objectobject import W_Object
class W_ProcObject(W_Object):
classdef = ClassDef("Proc", W_Object.classdef)
_immutable_fields_ = [
"bytecode", "w_self", "lexical_scope", "cells[*]",
"block", "parent_interp", "top_parent_interp",
"regex... |
50,415 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/objects/rangeobject.py | 21,23 | 6,43 | method_initialize | W_RangeObject | 23,32 | 23,32 | class W_RangeObject(W_Object):
classdef = ClassDef("Range", W_Object.classdef)
classdef.include_module(Enumerable)
_immutable_fields_ = ["w_start", "w_end", "exclusive"]
def __init__(self, space, w_start, w_end, exclusive):
W_Object.__init__(self, space)
self.w_start = w_start
s... | class W_RangeObject(W_Object):
classdef = ClassDef("Range", W_Object.classdef)
classdef.include_module(Enumerable)
_immutable_fields_ = ["w_start", "w_end", "exclusive"]
def __init__(self, space, w_start, w_end, exclusive):
W_Object.__init__(self, space)
self.w_start = w_start
s... | if self.w_start is not None or self.w_end is not None:
raise space.error(space.w_NameError, "`initialize' called twice")
if space.send(w_start, "<=>", [w_end]) is space.w_nil:
raise space.error(space.w_ArgumentError, "bad value for range")
self.w_start = w_start
... | if self.w_start is not None or self.w_end is not None:
raise space.error(space.w_NameError, "`initialize' called twice")
if space.send(w_start, "<=>", [w_end]) is space.w_nil:
raise space.error(space.w_ArgumentError, "bad value for range")
self.w_start = w_start
... | 36 | 323 | 132 | 455 | from topaz.module import ClassDef
from topaz.modules.enumerable import Enumerable
from topaz.objects.objectobject import W_Object
class W_RangeObject(W_Object):
classdef = ClassDef("Range", W_Object.classdef)
classdef.include_module(Enumerable)
_immutable_fields_ = ["w_start", "w_end", "exclusive"]
d... | from topaz.module import ClassDef
from topaz.modules.enumerable import Enumerable
from topaz.objects.objectobject import W_Object
class W_RangeObject(W_Object):
classdef = ClassDef("Range", W_Object.classdef)
classdef.include_module(Enumerable)
_immutable_fields_ = ["w_start", "w_end", "exclusive"]
d... |
50,416 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/objects/symbolobject.py | 63,65 | 6,83 | method_comparator | W_SymbolObject | 65,76 | 65,76 | class W_SymbolObject(W_Object):
_immutable_fields_ = ["symbol"]
classdef = ClassDef("Symbol", W_Object.classdef)
classdef.include_module(Comparable)
def __init__(self, space, symbol):
W_Object.__init__(self, space)
self.symbol = symbol
def __deepcopy__(self, memo):
obj = su... | class W_SymbolObject(W_Object):
_immutable_fields_ = ["symbol"]
classdef = ClassDef("Symbol", W_Object.classdef)
classdef.include_module(Comparable)
def __init__(self, space, symbol):
W_Object.__init__(self, space)
self.symbol = symbol
def __deepcopy__(self, memo):
obj = su... | if not space.is_kind_of(w_other, space.w_symbol):
return space.w_nil
s1 = self.symbol
s2 = space.symbol_w(w_other)
if s1 < s2:
return space.newint(-1)
elif s1 == s2:
return space.newint(0)
elif s1 > s2:
return space.newint(1... | if not space.is_kind_of(w_other, space.w_symbol):
return space.w_nil
s1 = self.symbol
s2 = space.symbol_w(w_other)
if s1 < s2:
return space.newint(-1)
elif s1 == s2:
return space.newint(0)
elif s1 > s2:
return space.newint(1... | 30 | 667 | 122 | 789 | from topaz.module import ClassDef
from topaz.modules.comparable import Comparable
from topaz.objects.objectobject import W_Object
class W_SymbolObject(W_Object):
_immutable_fields_ = ["symbol"]
classdef = ClassDef("Symbol", W_Object.classdef)
classdef.include_module(Comparable)
def __init__(self, spa... | from topaz.module import ClassDef
from topaz.modules.comparable import Comparable
from topaz.objects.objectobject import W_Object
class W_SymbolObject(W_Object):
_immutable_fields_ = ["symbol"]
classdef = ClassDef("Symbol", W_Object.classdef)
classdef.include_module(Comparable)
def __init__(self, spa... |
50,417 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/objects/threadobject.py | 28,30 | 7,53 | method_subscript_assign | W_ThreadObject | 30,33 | 30,33 | class W_ThreadObject(W_Object):
classdef = ClassDef("Thread", W_Object.classdef)
def __init__(self, space):
W_Object.__init__(self, space)
# TODO: This should be a map dict.
self.local_storage = {}
def __deepcopy__(self, memo):
obj = super(W_ThreadObject, self).__deepcopy__... | class W_ThreadObject(W_Object):
classdef = ClassDef("Thread", W_Object.classdef)
def __init__(self, space):
W_Object.__init__(self, space)
# TODO: This should be a map dict.
self.local_storage = {}
def __deepcopy__(self, memo):
obj = super(W_ThreadObject, self).__deepcopy__... | self.local_storage[key] = w_value
return w_value
| self.local_storage[key] = w_value
return w_value
| 5 | 519 | 24 | 543 | import copy
from topaz.module import ClassDef
from topaz.objects.objectobject import W_Object
class W_ThreadObject(W_Object):
classdef = ClassDef("Thread", W_Object.classdef)
def __init__(self, space):
W_Object.__init__(self, space)
# TODO: This should be a map dict.
self.local_stora... | import copy
from topaz.module import ClassDef
from topaz.objects.objectobject import W_Object
class W_ThreadObject(W_Object):
classdef = ClassDef("Thread", W_Object.classdef)
def __init__(self, space):
W_Object.__init__(self, space)
# TODO: This should be a map dict.
self.local_stora... |
50,418 | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz | /home/hieuvd/dung_repo/raw/stars_03/topazproject_topaz/topaz/topaz/tools/make_release.py | 9,10 | 1,30 | main | 10,28 | 10,28 | import os
import sys
import tarfile
PROJECT_ROOT = os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir)
def main(argv):
FILL_FUNC_BODYif __name__ == "__main__":
main(sys.argv[1:])
| import os
import sys
import tarfile
PROJECT_ROOT = os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir)
def main(argv):
FILL_FUNC_BODYif __name__ == "__main__":
main(sys.argv[1:])
| target = argv[0]
t = tarfile.open(target, mode="w:bz2")
for name in [
"bin/topaz.exe" if sys.platform == "win32" else "bin/topaz",
"lib-ruby",
"lib-topaz",
"AUTHORS.rst",
"LICENSE",
"README.rst"
]:
t.add(os.path.join(PROJECT_ROOT, name), arcname="t... | target = argv[0]
t = tarfile.open(target, mode="w:bz2")
for name in [
"bin/topaz.exe" if sys.platform == "win32" else "bin/topaz",
"lib-ruby",
"lib-topaz",
"AUTHORS.rst",
"LICENSE",
"README.rst"
]:
t.add(os.path.join(PROJECT_ROOT, name), arcname="t... | 45 | 92 | 214 | 306 | import os
import sys
import tarfile
PROJECT_ROOT = os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir)
def main(argv):
FILL_FUNC_BODYif __name__ == "__main__":
main(sys.argv[1:])
| import os
import sys
import tarfile
PROJECT_ROOT = os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir)
def main(argv):
FILL_FUNC_BODYif __name__ == "__main__":
main(sys.argv[1:])
| |
50,507 | /home/hieuvd/dung_repo/raw/stars_03/vchoutas_smplx/smplx | /home/hieuvd/dung_repo/raw/stars_03/vchoutas_smplx/smplx/smplx/joint_names.py | 291,293 | 272,321 | from_smplh | Body | 296,298 | 293,298 | class Body:
"""
Class for storing a single body pose.
"""
def __init__(self, joints, joint_names):
assert joints.ndim > 1
assert joints.shape[0] == len(joint_names)
self.joints = {}
for i, j in enumerate(joint_names):
self.joints[j] = joints[i]
@staticme... | class Body:
"""
Class for storing a single body pose.
"""
def __init__(self, joints, joint_names):
assert joints.ndim > 1
assert joints.shape[0] == len(joint_names)
self.joints = {}
for i, j in enumerate(joint_names):
self.joints[j] = joints[i]
@staticme... | return Body(joints, SMPLH_JOINT_NAMES)
| """
Create a Body object from SMPLH joints.
"""
return Body(joints, SMPLH_JOINT_NAMES)
| 3 | 413 | 22 | 435 | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... |
50,509 | /home/hieuvd/dung_repo/raw/stars_03/vchoutas_smplx/smplx | /home/hieuvd/dung_repo/raw/stars_03/vchoutas_smplx/smplx/smplx/utils.py | 120,121 | 1,127 | rot_mat_to_euler | 121,127 | 121,127 | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | # Calculates rotation matrix to euler angles
# Careful for extreme cases of eular angles like [0.0, pi, 0.0]
sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] +
rot_mats[:, 1, 0] * rot_mats[:, 1, 0])
return torch.atan2(-rot_mats[:, 2, 0], sy)
| # Calculates rotation matrix to euler angles
# Careful for extreme cases of eular angles like [0.0, pi, 0.0]
sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] +
rot_mats[:, 1, 0] * rot_mats[:, 1, 0])
return torch.atan2(-rot_mats[:, 2, 0], sy)
| 41 | 1,081 | 129 | 1,210 | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | |
50,510 | /home/hieuvd/dung_repo/raw/stars_03/vchoutas_smplx/smplx | /home/hieuvd/dung_repo/raw/stars_03/vchoutas_smplx/smplx/smplx/utils.py | 38,39 | 26,57 | get | ModelOutput | 39,41 | 39,41 | @dataclass
class ModelOutput:
vertices: Optional[Tensor] = None
joints: Optional[Tensor] = None
full_pose: Optional[Tensor] = None
global_orient: Optional[Tensor] = None
transl: Optional[Tensor] = None
v_shaped: Optional[Tensor] = None
def __getitem__(self, key):
return getattr(self... | @dataclass
class ModelOutput:
vertices: Optional[Tensor] = None
joints: Optional[Tensor] = None
full_pose: Optional[Tensor] = None
global_orient: Optional[Tensor] = None
transl: Optional[Tensor] = None
v_shaped: Optional[Tensor] = None
def __getitem__(self, key):
return getattr(self... | return getattr(self, key, default)
| return getattr(self, key, default)
| 4 | 253 | 15 | 268 | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... |
50,511 | /home/hieuvd/dung_repo/raw/stars_03/vchoutas_smplx/smplx | /home/hieuvd/dung_repo/raw/stars_03/vchoutas_smplx/smplx/smplx/vertex_joint_selector.py | 73,74 | 29,81 | forward | VertexJointSelector | 74,81 | 74,81 | class VertexJointSelector(nn.Module):
def __init__(self, vertex_ids=None,
use_hands=True,
use_feet_keypoints=True, **kwargs):
super(VertexJointSelector, self).__init__()
extra_joints_idxs = []
face_keyp_idxs = np.array([
vertex_ids['nose'],
... | class VertexJointSelector(nn.Module):
def __init__(self, vertex_ids=None,
use_hands=True,
use_feet_keypoints=True, **kwargs):
super(VertexJointSelector, self).__init__()
extra_joints_idxs = []
face_keyp_idxs = np.array([
vertex_ids['nose'],
... | extra_joints = torch.index_select(vertices, 1, self.extra_joints_idxs.to(torch.long)) #The '.to(torch.long)'.
# added to make the trace work in c++,
... | extra_joints = torch.index_select(vertices, 1, self.extra_joints_idxs.to(torch.long)) #The '.to(torch.long)'.
# added to make the trace work in c++,
... | 41 | 507 | 134 | 641 | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... |
50,512 | /home/hieuvd/dung_repo/raw/stars_03/vchoutas_smplx/smplx | /home/hieuvd/dung_repo/raw/stars_03/vchoutas_smplx/smplx/tools/merge_smplh_mano.py | 29,31 | 1,102 | merge_models | 31,78 | 31,78 | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... |
if smplh_fn.endswith('.pkl'):
with open(smplh_fn, 'rb') as body_file:
body_data = pickle.load(body_file, encoding='latin1')
elif smplh_fn.endswith('.npz'):
body_data_np = np.load(smplh_fn)
body_data = {}
for key in body_data_np:
body_data[key] = body_data... |
if smplh_fn.endswith('.pkl'):
with open(smplh_fn, 'rb') as body_file:
body_data = pickle.load(body_file, encoding='latin1')
elif smplh_fn.endswith('.npz'):
body_data_np = np.load(smplh_fn)
body_data = {}
for key in body_data_np:
body_data[key] = body_data... | 120 | 664 | 639 | 1,303 | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | |
50,513 | /home/hieuvd/dung_repo/raw/stars_03/vchoutas_smplx/smplx | /home/hieuvd/dung_repo/raw/stars_03/vchoutas_smplx/smplx/transfer_model/losses/utils.py | 19,20 | 1,28 | get_reduction_method | 20,28 | 20,28 | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | if reduction == 'mean':
return torch.mean
elif reduction == 'sum':
return torch.sum
elif reduction == 'none':
return lambda x: x
else:
raise ValueError('Unknown reduction method: {}'.format(reduction))
| if reduction == 'mean':
return torch.mean
elif reduction == 'sum':
return torch.sum
elif reduction == 'none':
return lambda x: x
else:
raise ValueError('Unknown reduction method: {}'.format(reduction))
| 26 | 255 | 75 | 330 | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | |
50,514 | /home/hieuvd/dung_repo/raw/stars_03/vchoutas_smplx/smplx | /home/hieuvd/dung_repo/raw/stars_03/vchoutas_smplx/smplx/transfer_model/utils/metrics.py | 21,22 | 1,26 | v2v | 22,26 | 22,26 | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | if torch.is_tensor(x):
return (x - y).pow(2).sum(dim=-1).sqrt().mean()
else:
return np.sqrt(np.power(x - y, 2)).sum(axis=-1).mean()
| if torch.is_tensor(x):
return (x - y).pow(2).sum(dim=-1).sqrt().mean()
else:
return np.sqrt(np.power(x - y, 2)).sum(axis=-1).mean()
| 12 | 256 | 70 | 326 | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | |
50,515 | /home/hieuvd/dung_repo/raw/stars_03/vchoutas_smplx/smplx | /home/hieuvd/dung_repo/raw/stars_03/vchoutas_smplx/smplx/transfer_model/utils/np_utils.py | 23,24 | 1,35 | rel_change | 24,27 | 24,27 | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | return (prev_val - curr_val) / max([np.abs(prev_val), np.abs(curr_val), 1])
| return (prev_val - curr_val) / max([np.abs(prev_val), np.abs(curr_val), 1])
| 8 | 363 | 41 | 404 | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | |
50,516 | /home/hieuvd/dung_repo/raw/stars_03/vchoutas_smplx/smplx | /home/hieuvd/dung_repo/raw/stars_03/vchoutas_smplx/smplx/transfer_model/utils/torch_utils.py | 21,22 | 1,25 | from_torch | 22,25 | 22,25 | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | if torch.is_tensor(x):
x = x.detach().cpu().numpy()
return x.astype(dtype)
| if torch.is_tensor(x):
x = x.detach().cpu().numpy()
return x.astype(dtype)
| 7 | 264 | 41 | 305 | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | |
50,517 | /home/hieuvd/dung_repo/raw/stars_03/wapiflapi_binglide/binglide | /home/hieuvd/dung_repo/raw/stars_03/wapiflapi_binglide/binglide/binglide/levelers.py | 32,34 | 1,61 | lvl_cumul256proj_NDS | 34,61 | 34,61 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
from functools import reduce
from operator import itemgetter, mul
import numpy as np
from binglide.renderers import Renderer
@Renderer.register_leveler(None)
def lvl_256proj(dst, data, shape):
l = np.amin(data)
h = np.amax(data)
print("leveling: %s %s" % ... | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
from functools import reduce
from operator import itemgetter, mul
import numpy as np
from binglide.renderers import Renderer
@Renderer.register_leveler(None)
def lvl_256proj(dst, data, shape):
l = np.amin(data)
h = np.amax(data)
print("leveling: %s %s" % ... |
print("leveling %s" % (shape,))
print("\tsorting. %d / %d = %d%%" % (
len(data), reduce(mul, shape),
len(data) * 100 / reduce(mul, shape)))
tgrams = sorted(data.items(), key=itemgetter(1))
print("\tdone sorting.")
hist = np.zeros(shape)
nbtg = float(sum(t[1] for t in ... |
print("leveling %s" % (shape,))
print("\tsorting. %d / %d = %d%%" % (
len(data), reduce(mul, shape),
len(data) * 100 / reduce(mul, shape)))
tgrams = sorted(data.items(), key=itemgetter(1))
print("\tdone sorting.")
hist = np.zeros(shape)
nbtg = float(sum(t[1] for t in ... | 73 | 290 | 255 | 545 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
from functools import reduce
from operator import itemgetter, mul
import numpy as np
from binglide.renderers import Renderer
@Renderer.register_leveler(None)
def lvl_256proj(dst, data, shape):
l = np.amin(data)
h = np.amax(data)
print("leveling: %s %s" % ... | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
from functools import reduce
from operator import itemgetter, mul
import numpy as np
from binglide.renderers import Renderer
@Renderer.register_leveler(None)
def lvl_256proj(dst, data, shape):
l = np.amin(data)
h = np.amax(data)
print("leveling: %s %s" % ... | |
50,518 | /home/hieuvd/dung_repo/raw/stars_03/wapiflapi_binglide/binglide | /home/hieuvd/dung_repo/raw/stars_03/wapiflapi_binglide/binglide/binglide/mixers.py | 13,14 | 8,20 | get_coefs | ChannelMixer | 14,16 | 14,16 | class ChannelMixer(object):
def __init__(self, reds=False, greens=False, blues=False, alpha=False):
self.channels = [i for i, v in enumerate((reds, greens, blues, alpha)) if v]
def get_coefs(self):
FILL_FUNC_BODY def __call__(self, colored, data, coefs):
for c in self.channels:
... | class ChannelMixer(object):
def __init__(self, reds=False, greens=False, blues=False, alpha=False):
self.channels = [i for i, v in enumerate((reds, greens, blues, alpha)) if v]
def get_coefs(self):
FILL_FUNC_BODY def __call__(self, colored, data, coefs):
for c in self.channels:
... | return [float(c in self.channels) for c in range(4)]
| return [float(c in self.channels) for c in range(4)]
| 8 | 133 | 23 | 156 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
from binglide.renderers import Renderer
class ChannelMixer(object):
def __init__(self, reds=False, greens=False, blues=False, alpha=False):
self.channels = [i for i, v in enumerate((reds, greens, blues, alpha)) if v]
def get_coefs(se... | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
from binglide.renderers import Renderer
class ChannelMixer(object):
def __init__(self, reds=False, greens=False, blues=False, alpha=False):
self.channels = [i for i, v in enumerate((reds, greens, blues, alpha)) if v]
def get_coefs(se... |
50,519 | /home/hieuvd/dung_repo/raw/stars_03/wapiflapi_binglide/binglide | /home/hieuvd/dung_repo/raw/stars_03/wapiflapi_binglide/binglide/binglide/painters.py | 51,53 | 1,76 | paint_asciiposx | 53,76 | 53,76 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
from binglide.renderers import Renderer
@Renderer.register_painter(None)
def paint_values(colored, data, mixers, coefs):
for mixer in mixers:
mixer(colored, data, coefs)
return colored
@Renderer.register_painter(None)
def paint_ascii(... | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
from binglide.renderers import Renderer
@Renderer.register_painter(None)
def paint_values(colored, data, mixers, coefs):
for mixer in mixers:
mixer(colored, data, coefs)
return colored
@Renderer.register_painter(None)
def paint_ascii(... | ndata = np.zeros(data.shape)
print(data.shape)
if len(data.shape) == 3:
l, h = 0x20 * data.shape[0] // 256, 0x7e * data.shape[0] // 256
ndata[l:h, 0:-1, 0:-1] += data[l:h, 0:-1, 0:-1] * 0.33
l, h = 0x20 * data.shape[1] // 256, 0x7e * data.shape[1] // 256
ndata[0:-1, l:h, 0:... | ndata = np.zeros(data.shape)
print(data.shape)
if len(data.shape) == 3:
l, h = 0x20 * data.shape[0] // 256, 0x7e * data.shape[0] // 256
ndata[l:h, 0:-1, 0:-1] += data[l:h, 0:-1, 0:-1] * 0.33
l, h = 0x20 * data.shape[1] // 256, 0x7e * data.shape[1] // 256
ndata[0:-1, l:h, 0:... | 136 | 717 | 514 | 1,231 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
from binglide.renderers import Renderer
@Renderer.register_painter(None)
def paint_values(colored, data, mixers, coefs):
for mixer in mixers:
mixer(colored, data, coefs)
return colored
@Renderer.register_painter(None)
def paint_ascii(... | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
from binglide.renderers import Renderer
@Renderer.register_painter(None)
def paint_values(colored, data, mixers, coefs):
for mixer in mixers:
mixer(colored, data, coefs)
return colored
@Renderer.register_painter(None)
def paint_ascii(... | |
50,520 | /home/hieuvd/dung_repo/raw/stars_03/wapiflapi_binglide/binglide | /home/hieuvd/dung_repo/raw/stars_03/wapiflapi_binglide/binglide/binglide/projectors.py | 12,14 | 1,80 | proj_scanline | 14,43 | 14,43 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import operator
from functools import reduce
from math import floor, ceil
import numpy as np
from binglide.renderers import Renderer
@Renderer.register_projector((("1D", "2D", "3D"), ("1D", "2D", "3D")))
def proj_scanline(dst, data, shape):
FILL_FUNC_BODYdef proj_scanl... | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import operator
from functools import reduce
from math import floor, ceil
import numpy as np
from binglide.renderers import Renderer
@Renderer.register_projector((("1D", "2D", "3D"), ("1D", "2D", "3D")))
def proj_scanline(dst, data, shape):
FILL_FUNC_BODYdef proj_scanl... |
# We get colored data. Carefull for the extra dimension.
# Shape doesnt have RGBA dimension.
size = data.size / 4 # Ignore RGBA values.
ratio = size / reduce(operator.mul, shape)
ratio = pow(ratio, 1/len(shape))
rshape_c = [int(ceil(ratio * x)) for x in shape]
rshape = [int(ratio * x) fo... |
# We get colored data. Carefull for the extra dimension.
# Shape doesnt have RGBA dimension.
size = data.size / 4 # Ignore RGBA values.
ratio = size / reduce(operator.mul, shape)
ratio = pow(ratio, 1/len(shape))
rshape_c = [int(ceil(ratio * x)) for x in shape]
rshape = [int(ratio * x) fo... | 106 | 503 | 296 | 799 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import operator
from functools import reduce
from math import floor, ceil
import numpy as np
from binglide.renderers import Renderer
@Renderer.register_projector((("1D", "2D", "3D"), ("1D", "2D", "3D")))
def proj_scanline(dst, data, shape):
FILL_FUNC_BODYdef proj_scanl... | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import operator
from functools import reduce
from math import floor, ceil
import numpy as np
from binglide.renderers import Renderer
@Renderer.register_projector((("1D", "2D", "3D"), ("1D", "2D", "3D")))
def proj_scanline(dst, data, shape):
FILL_FUNC_BODYdef proj_scanl... | |
50,521 | /home/hieuvd/dung_repo/raw/stars_03/wapiflapi_binglide/binglide | /home/hieuvd/dung_repo/raw/stars_03/wapiflapi_binglide/binglide/binglide/utils.py | 2,3 | 1,4 | ddim_from_shape | 3,4 | 3,4 |
def ddim_from_shape(shape):
FILL_FUNC_BODY |
def ddim_from_shape(shape):
FILL_FUNC_BODY | return "%dD" % len(shape)
| return "%dD" % len(shape)
| 4 | 22 | 13 | 35 |
def ddim_from_shape(shape):
FILL_FUNC_BODY |
def ddim_from_shape(shape):
FILL_FUNC_BODY | |
50,523 | /home/hieuvd/dung_repo/raw/stars_03/weaveworks_kubediff/kubediff | /home/hieuvd/dung_repo/raw/stars_03/weaveworks_kubediff/kubediff/kubedifflib/_diff.py | 41,42 | 33,52 | to_text | Difference | 42,52 | 42,52 | class Difference(object):
"""An observed difference."""
def __init__(self, message, path, *args):
self.message = message
self.path = path
self.args = args
def to_text(self, kind=''):
FILL_FUNC_BODY | class Difference(object):
"""An observed difference."""
def __init__(self, message, path, *args):
self.message = message
self.path = path
self.args = args
def to_text(self, kind=''):
FILL_FUNC_BODY | if kind.startswith('Secret.') and len(self.args) == 2:
message = self.message % (mask(self.args[0]), mask(self.args[1]))
else:
message = self.message % self.args
if self.path is None:
return message
return '%s: %s' % (self.path, message)
| if kind.startswith('Secret.') and len(self.args) == 2:
message = self.message % (mask(self.args[0]), mask(self.args[1]))
else:
message = self.message % self.args
if self.path is None:
return message
return '%s: %s' % (self.path, message)
| 30 | 75 | 106 | 181 | # -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function,
unicode_literals)
from ._kube import (
KubeObject,
iter_files,
)
import yaml
import sys
import subprocess
import os
import operator
import numbers
import json
import difflib
import collections
fro... | # -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function,
unicode_literals)
from ._kube import (
KubeObject,
iter_files,
)
import yaml
import sys
import subprocess
import os
import operator
import numbers
import json
import difflib
import collections
fro... |
50,524 | /home/hieuvd/dung_repo/raw/stars_03/weaveworks_kubediff/kubediff | /home/hieuvd/dung_repo/raw/stars_03/weaveworks_kubediff/kubediff/kubedifflib/_diff.py | 250,251 | 243,257 | diff | JSONPrinter | 251,253 | 251,253 | class JSONPrinter(object):
def __init__(self):
self.data = collections.defaultdict(list)
def add(self, path, kube_obj):
pass
def diff(self, path, difference):
FILL_FUNC_BODY def finish(self):
print(json.dumps(self.data, sort_keys=True, indent=2, separators=(',', ': ')))
| class JSONPrinter(object):
def __init__(self):
self.data = collections.defaultdict(list)
def add(self, path, kube_obj):
pass
def diff(self, path, difference):
FILL_FUNC_BODY def finish(self):
print(json.dumps(self.data, sort_keys=True, indent=2, separators=(',', ': ')))
| self.data[path].append(difference.to_text())
| self.data[path].append(difference.to_text())
| 1 | 111 | 20 | 131 | # -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function,
unicode_literals)
from ._kube import (
KubeObject,
iter_files,
)
import yaml
import sys
import subprocess
import os
import operator
import numbers
import json
import difflib
import collections
fro... | # -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function,
unicode_literals)
from ._kube import (
KubeObject,
iter_files,
)
import yaml
import sys
import subprocess
import os
import operator
import numbers
import json
import difflib
import collections
fro... |
50,525 | /home/hieuvd/dung_repo/raw/stars_03/weaveworks_kubediff/kubediff | /home/hieuvd/dung_repo/raw/stars_03/weaveworks_kubediff/kubediff/kubedifflib/_diff.py | 231,232 | 216,243 | diff | QuietTextPrinter | 232,239 | 232,239 | class QuietTextPrinter(object):
"""Only output if there's a difference."""
def __init__(self, stream=None):
self._stream = stream if stream else sys.stdout
self._current = None
def _write(self, msg, *args):
self._stream.write(msg % args)
self._stream.write('\n')
sel... | class QuietTextPrinter(object):
"""Only output if there's a difference."""
def __init__(self, stream=None):
self._stream = stream if stream else sys.stdout
self._current = None
def _write(self, msg, *args):
self._stream.write(msg % args)
self._stream.write('\n')
sel... | if self._current:
self._write('## %s (%s)', self._current.namespaced_name, self._current.kind)
else:
self._write('## UNKNOWN')
self._write('')
self._write('%s', difference.to_text(self._current.kind))
| if self._current:
self._write('## %s (%s)', self._current.namespaced_name, self._current.kind)
else:
self._write('## UNKNOWN')
self._write('')
self._write('%s', difference.to_text(self._current.kind))
| 13 | 158 | 89 | 247 | # -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function,
unicode_literals)
from ._kube import (
KubeObject,
iter_files,
)
import yaml
import sys
import subprocess
import os
import operator
import numbers
import json
import difflib
import collections
fro... | # -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function,
unicode_literals)
from ._kube import (
KubeObject,
iter_files,
)
import yaml
import sys
import subprocess
import os
import operator
import numbers
import json
import difflib
import collections
fro... |
50,526 | /home/hieuvd/dung_repo/raw/stars_03/weaveworks_kubediff/kubediff | /home/hieuvd/dung_repo/raw/stars_03/weaveworks_kubediff/kubediff/kubedifflib/_diff.py | 209,210 | 205,216 | diff | StdoutPrinter | 210,212 | 210,212 | class StdoutPrinter(object):
def add(self, _, kube_obj):
print("Checking %s '%s'" % (kube_obj.kind, kube_obj.namespaced_name))
def diff(self, _, difference):
FILL_FUNC_BODY def finish(self):
pass
| class StdoutPrinter(object):
def add(self, _, kube_obj):
print("Checking %s '%s'" % (kube_obj.kind, kube_obj.namespaced_name))
def diff(self, _, difference):
FILL_FUNC_BODY def finish(self):
pass
| print(" *** " + difference.to_text())
| print(" *** " + difference.to_text())
| 5 | 87 | 16 | 103 | # -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function,
unicode_literals)
from ._kube import (
KubeObject,
iter_files,
)
import yaml
import sys
import subprocess
import os
import operator
import numbers
import json
import difflib
import collections
fro... | # -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function,
unicode_literals)
from ._kube import (
KubeObject,
iter_files,
)
import yaml
import sys
import subprocess
import os
import operator
import numbers
import json
import difflib
import collections
fro... |
50,527 | /home/hieuvd/dung_repo/raw/stars_03/weaveworks_kubediff/kubediff | /home/hieuvd/dung_repo/raw/stars_03/weaveworks_kubediff/kubediff/kubedifflib/_images.py | 55,56 | 1,107 | get_differing_images | 65,89 | 56,89 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function,
unicode_literals)
from ._kube import (
KubeObject,
iter_files,
)
import yaml
import os
import attr
from future.utils import viewitems
from builtins import object
from builtin... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function,
unicode_literals)
from ._kube import (
KubeObject,
iter_files,
)
import yaml
import os
import attr
from future.utils import viewitems
from builtins import object
from builtin... | source_objs = frozenset(source_env)
target_objs = frozenset(target_env)
# XXX: What about missing objects?
diffs = {}
for obj in source_objs & target_objs:
source_images = list(map(Image.parse, sorted(iter_images(source_env[obj]))))
target_images = list(map(Image.parse, sorted(iter_i... | """Return the images that differ between Kubernetes environments.
:param Dict[KubeObject, Dict] source_env: The Kubernetes objects in the
source environment.
:param Dict[KubeObject, Dict] target_env: The Kubernetes objects in the
target environment.
:return: A dictionary mapping image n... | 108 | 659 | 343 | 1,002 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function,
unicode_literals)
from ._kube import (
KubeObject,
iter_files,
)
import yaml
import os
import attr
from future.utils import viewitems
from builtins import object
from builtin... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function,
unicode_literals)
from ._kube import (
KubeObject,
iter_files,
)
import yaml
import os
import attr
from future.utils import viewitems
from builtins import object
from builtin... | |
50,528 | /home/hieuvd/dung_repo/raw/stars_03/weaveworks_kubediff/kubediff | /home/hieuvd/dung_repo/raw/stars_03/weaveworks_kubediff/kubediff/kubedifflib/_images.py | 45,47 | 39,55 | parse | Image | 47,55 | 47,55 | @attr.s
class Image(object):
"""A Docker image name."""
name = attr.ib()
label = attr.ib()
@classmethod
def parse(cls, image_name):
FILL_FUNC_BODY | @attr.s
class Image(object):
"""A Docker image name."""
name = attr.ib()
label = attr.ib()
@classmethod
def parse(cls, image_name):
FILL_FUNC_BODY | try:
[name, label] = image_name.split(':', 1)
except ValueError:
name = image_name
label = "latest"
return cls(name, label)
| try:
[name, label] = image_name.split(':', 1)
except ValueError:
name = image_name
label = "latest"
return cls(name, label)
| 17 | 63 | 63 | 126 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function,
unicode_literals)
from ._kube import (
KubeObject,
iter_files,
)
import yaml
import os
import attr
from future.utils import viewitems
from builtins import object
from builtin... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function,
unicode_literals)
from ._kube import (
KubeObject,
iter_files,
)
import yaml
import os
import attr
from future.utils import viewitems
from builtins import object
from builtin... |
50,529 | /home/hieuvd/dung_repo/raw/stars_03/weaveworks_kubediff/kubediff | /home/hieuvd/dung_repo/raw/stars_03/weaveworks_kubediff/kubediff/kubedifflib/_kube.py | 13,14 | 1,91 | iter_files | 21,31 | 14,31 | # -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function,
unicode_literals)
import logging
import yaml
import subprocess
import os
import attr
from builtins import object
def iter_files(paths):
FILL_FUNC_BODY@attr.s
class KubeObject(object):
"""A Kubernete... | # -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function,
unicode_literals)
import logging
import yaml
import subprocess
import os
import attr
from builtins import object
def iter_files(paths):
"""Yield absolute paths to all the files in 'paths'.
'pat... | # XXX: Copied from service/monitoring/lint
for path in paths:
if os.path.isfile(path):
yield path
else:
for root, _, filenames in os.walk(path):
for filename in filenames:
yield os.path.join(root, filename)
| """Yield absolute paths to all the files in 'paths'.
'paths' is expected to be an iterable of paths to files or directories.
Paths to files are yielded as is, paths to directories are recursed into.
Equivalent to ``find "$paths[@]" -type f``.
"""
# XXX: Copied from service/monitoring/lint
... | 27 | 916 | 92 | 1,008 | # -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function,
unicode_literals)
import logging
import yaml
import subprocess
import os
import attr
from builtins import object
def iter_files(paths):
FILL_FUNC_BODY@attr.s
class KubeObject(object):
"""A Kubernete... | # -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function,
unicode_literals)
import logging
import yaml
import subprocess
import os
import attr
from builtins import object
def iter_files(paths):
"""Yield absolute paths to all the files in 'paths'.
'pat... | |
50,530 | /home/hieuvd/dung_repo/raw/stars_03/weaveworks_kubediff/kubediff | /home/hieuvd/dung_repo/raw/stars_03/weaveworks_kubediff/kubediff/kubedifflib/_kube.py | 76,77 | 31,91 | get_from_cluster | KubeObject | 83,91 | 77,91 | @attr.s
class KubeObject(object):
"""A Kubernetes object."""
namespace = attr.ib()
kind = attr.ib()
name = attr.ib()
data = attr.ib()
@classmethod
def from_dict(cls, data, namespace=""):
"""Construct a 'KubeObject' from a dictionary of Kubernetes data.
:param dict data: Ku... | @attr.s
class KubeObject(object):
"""A Kubernetes object."""
namespace = attr.ib()
kind = attr.ib()
name = attr.ib()
data = attr.ib()
@classmethod
def from_dict(cls, data, namespace=""):
"""Construct a 'KubeObject' from a dictionary of Kubernetes data.
:param dict data: Ku... | args = ["--namespace=%s" % self.namespace, "-o=yaml"]
if kubeconfig is not None:
args.append("--kubeconfig=%s" % kubeconfig)
if context is not None:
args.append("--context=%s" % context)
running = subprocess.check_output(["kubectl", "get"] + args + [self.kind, se... | """Fetch data for this object from a Kubernetes cluster.
:param str kubeconfig: Path to a Kubernetes configuration file. If None,
fetches data from the default cluster.
:return: A dict of data for this Kubernetes object.
"""
args = ["--namespace=%s" % self.namespace,... | 34 | 638 | 137 | 775 | # -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function,
unicode_literals)
import logging
import yaml
import subprocess
import os
import attr
from builtins import object
def iter_files(paths):
"""Yield absolute paths to all the files in 'paths'.
'pat... | # -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function,
unicode_literals)
import logging
import yaml
import subprocess
import os
import attr
from builtins import object
def iter_files(paths):
"""Yield absolute paths to all the files in 'paths'.
'pat... |
50,542 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/annotation.py | 82,83 | 23,107 | save_annotation | Annotation | 83,107 | 83,107 | class Annotation:
def __init__(self, image_path, label_path):
img_folder, img_name = os.path.split(image_path)
self.description = 'ISAT'
self.img_folder = img_folder
self.img_name = img_name
self.label_path = label_path
self.note = ''
image = np.array(Image.o... | class Annotation:
def __init__(self, image_path, label_path):
img_folder, img_name = os.path.split(image_path)
self.description = 'ISAT'
self.img_folder = img_folder
self.img_name = img_name
self.label_path = label_path
self.note = ''
image = np.array(Image.o... | dataset = {}
dataset['info'] = {}
dataset['info']['description'] = self.description
dataset['info']['folder'] = self.img_folder
dataset['info']['name'] = self.img_name
dataset['info']['width'] = self.width
dataset['info']['height'] = self.height
dataset['i... | dataset = {}
dataset['info'] = {}
dataset['info']['description'] = self.description
dataset['info']['folder'] = self.img_folder
dataset['info']['name'] = self.img_name
dataset['info']['width'] = self.width
dataset['info']['height'] = self.height
dataset['i... | 72 | 708 | 281 | 989 | # -*- coding: utf-8 -*-
# @Author : LG
import os
from PIL import Image
import numpy as np
from json import load, dump
from typing import List
class Object:
def __init__(self, category:str, group:int, segmentation, area, layer, bbox, iscrowd=0, note=''):
self.category = category
self.group = grou... | # -*- coding: utf-8 -*-
# @Author : LG
import os
from PIL import Image
import numpy as np
from json import load, dump
from typing import List
class Object:
def __init__(self, category:str, group:int, segmentation, area, layer, bbox, iscrowd=0, note=''):
self.category = category
self.group = grou... |
50,543 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/configs.py | 25,26 | 1,58 | save_config | 26,31 | 26,31 | import yaml
from enum import Enum
import os
ISAT_ROOT = os.path.split(os.path.abspath(__file__))[0]
SOFTWARE_CONFIG_FILE = os.path.join(ISAT_ROOT, 'software.yaml')
CONFIG_FILE = os.path.join(ISAT_ROOT, 'isat.yaml')
CHECKPOINT_PATH = os.path.join(ISAT_ROOT, 'checkpoints')
os.makedirs(os.path.join(CHECKPOINT_PATH, 'tmp... | import yaml
from enum import Enum
import os
ISAT_ROOT = os.path.split(os.path.abspath(__file__))[0]
SOFTWARE_CONFIG_FILE = os.path.join(ISAT_ROOT, 'software.yaml')
CONFIG_FILE = os.path.join(ISAT_ROOT, 'isat.yaml')
CHECKPOINT_PATH = os.path.join(ISAT_ROOT, 'checkpoints')
os.makedirs(os.path.join(CHECKPOINT_PATH, 'tmp... | s = yaml.dump(cfg)
with open(file, 'w') as f:
f.write(s)
return True
| s = yaml.dump(cfg)
with open(file, 'w') as f:
f.write(s)
return True
| 11 | 534 | 42 | 576 | import yaml
from enum import Enum
import os
ISAT_ROOT = os.path.split(os.path.abspath(__file__))[0]
SOFTWARE_CONFIG_FILE = os.path.join(ISAT_ROOT, 'software.yaml')
CONFIG_FILE = os.path.join(ISAT_ROOT, 'isat.yaml')
CHECKPOINT_PATH = os.path.join(ISAT_ROOT, 'checkpoints')
os.makedirs(os.path.join(CHECKPOINT_PATH, 'tmp... | import yaml
from enum import Enum
import os
ISAT_ROOT = os.path.split(os.path.abspath(__file__))[0]
SOFTWARE_CONFIG_FILE = os.path.join(ISAT_ROOT, 'software.yaml')
CONFIG_FILE = os.path.join(ISAT_ROOT, 'isat.yaml')
CHECKPOINT_PATH = os.path.join(ISAT_ROOT, 'checkpoints')
os.makedirs(os.path.join(CHECKPOINT_PATH, 'tmp... | |
50,545 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/main.py | 14,15 | 1,23 | main | 15,20 | 15,20 | # -*- coding: utf-8 -*-
# @Author : LG
# import os
from PyQt5 import QtWidgets
from ISAT.widgets.mainwindow import MainWindow
import sys
# ubuntu18.04装其他应用时，环境崩了。。。qtapp初始化后，torch调用cuda会卡死，但先调用下cuda就不会卡死。
import torch
torch.cuda.is_available()
# (知道怎么处理该问题，请反馈下 T.T)
def main():
FILL_FUNC_BODYif __name__ == '__main_... | # -*- coding: utf-8 -*-
# @Author : LG
# import os
from PyQt5 import QtWidgets
from ISAT.widgets.mainwindow import MainWindow
import sys
# ubuntu18.04装其他应用时，环境崩了。。。qtapp初始化后，torch调用cuda会卡死，但先调用下cuda就不会卡死。
import torch
torch.cuda.is_available()
# (知道怎么处理该问题，请反馈下 T.T)
def main():
FILL_FUNC_BODYif __name__ == '__main_... | app = QtWidgets.QApplication([''])
mainwindow = MainWindow()
mainwindow.show()
sys.exit(app.exec_())
| app = QtWidgets.QApplication([''])
mainwindow = MainWindow()
mainwindow.show()
sys.exit(app.exec_())
| 8 | 164 | 44 | 208 | # -*- coding: utf-8 -*-
# @Author : LG
# import os
from PyQt5 import QtWidgets
from ISAT.widgets.mainwindow import MainWindow
import sys
# ubuntu18.04装其他应用时，环境崩了。。。qtapp初始化后，torch调用cuda会卡死，但先调用下cuda就不会卡死。
import torch
torch.cuda.is_available()
# (知道怎么处理该问题，请反馈下 T.T)
def main():
FILL_FUNC_BODYif __name__ == '__main_... | # -*- coding: utf-8 -*-
# @Author : LG
# import os
from PyQt5 import QtWidgets
from ISAT.widgets.mainwindow import MainWindow
import sys
# ubuntu18.04装其他应用时，环境崩了。。。qtapp初始化后，torch调用cuda会卡死，但先调用下cuda就不会卡死。
import torch
torch.cuda.is_available()
# (知道怎么处理该问题，请反馈下 T.T)
def main():
FILL_FUNC_BODYif __name__ == '__main_... | |
50,547 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/scripts/labelme.py | 51,52 | 10,124 | _save_one_labelme_json | LABELME | 52,88 | 52,88 | class LABELME(ISAT):
def __init__(self):
self.keep_crowd = True
def read_from_LABELME(self, json_root):
self.annos.clear()
self.cates = ()
json_files = [file for file in os.listdir(json_root) if file.endswith('.json')]
pbar = tqdm.tqdm(json_files)
for file in p... | class LABELME(ISAT):
def __init__(self):
self.keep_crowd = True
def read_from_LABELME(self, json_root):
self.annos.clear()
self.cates = ()
json_files = [file for file in os.listdir(json_root) if file.endswith('.json')]
pbar = tqdm.tqdm(json_files)
for file in p... | labelme_anno = {}
labelme_anno['version'] = "5.2.0.post4 | ISAT to LabelMe"
labelme_anno['imagePath'] = anno.info.name
labelme_anno['imageData'] = None
labelme_anno['imageHeight'] = anno.info.height
labelme_anno['imageWidth'] = anno.info.width
labelme_anno['flags'... | labelme_anno = {}
labelme_anno['version'] = "5.2.0.post4 | ISAT to LabelMe"
labelme_anno['imagePath'] = anno.info.name
labelme_anno['imageData'] = None
labelme_anno['imageHeight'] = anno.info.height
labelme_anno['imageWidth'] = anno.info.width
labelme_anno['flags'... | 98 | 940 | 386 | 1,326 | # -*- coding: utf-8 -*-
# @Author : LG
from ISAT.scripts.isat import ISAT
from json import dump, load
import tqdm
import os
class LABELME(ISAT):
def __init__(self):
self.keep_crowd = True
def read_from_LABELME(self, json_root):
self.annos.clear()
self.cates = ()
json_files ... | # -*- coding: utf-8 -*-
# @Author : LG
from ISAT.scripts.isat import ISAT
from json import dump, load
import tqdm
import os
class LABELME(ISAT):
def __init__(self):
self.keep_crowd = True
def read_from_LABELME(self, json_root):
self.annos.clear()
self.cates = ()
json_files ... |
50,548 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/scripts/voc.py | 39,40 | 13,87 | _save_one_voc_png | VOC | 40,81 | 40,81 | class VOC(ISAT):
def __init__(self):
self.keep_crowd = True
self.is_instance = False
def save_to_voc(self, png_root):
os.makedirs(png_root, exist_ok=True)
# cmap
cmap = imgviz.label_colormap()
category_index_dict = {}
with open(os.path.join(png_root, 'c... | class VOC(ISAT):
def __init__(self):
self.keep_crowd = True
self.is_instance = False
def save_to_voc(self, png_root):
os.makedirs(png_root, exist_ok=True)
# cmap
cmap = imgviz.label_colormap()
category_index_dict = {}
with open(os.path.join(png_root, 'c... | info = anno.info
objects = anno.objs
img_name = info.name
width = info.width
height = info.height
depth = info.depth
note = info.note
img = np.zeros(shape=(height, width), dtype=np.uint8)
objects = sorted(objects, key=lambda obj:obj.layer)
... | info = anno.info
objects = anno.objs
img_name = info.name
width = info.width
height = info.height
depth = info.depth
note = info.note
img = np.zeros(shape=(height, width), dtype=np.uint8)
objects = sorted(objects, key=lambda obj:obj.layer)
... | 143 | 433 | 480 | 913 | # -*- coding: utf-8 -*-
# @Author : LG
from ISAT.scripts.isat import ISAT
import tqdm
import numpy as np
from PIL import Image
import imgviz
from skimage.draw.draw import polygon
import os
class VOC(ISAT):
def __init__(self):
self.keep_crowd = True
self.is_instance = False
def save_to_voc(... | # -*- coding: utf-8 -*-
# @Author : LG
from ISAT.scripts.isat import ISAT
import tqdm
import numpy as np
from PIL import Image
import imgviz
from skimage.draw.draw import polygon
import os
class VOC(ISAT):
def __init__(self):
self.keep_crowd = True
self.is_instance = False
def save_to_voc(... |
50,549 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/scripts/voc_detection.py | 27,28 | 10,121 | _save_one_voc_xml | VOCDetect | 28,121 | 28,121 | class VOCDetect(ISAT):
def __init__(self):
self.keep_crowd = True
def save_to_XML(self, xml_root):
os.makedirs(xml_root, exist_ok=True)
pbar = tqdm.tqdm(self.annos.items())
for name_without_suffix, anno in pbar:
xml_path = os.path.join(xml_root, name_without_suffix ... | class VOCDetect(ISAT):
def __init__(self):
self.keep_crowd = True
def save_to_XML(self, xml_root):
os.makedirs(xml_root, exist_ok=True)
pbar = tqdm.tqdm(self.annos.items())
for name_without_suffix, anno in pbar:
xml_path = os.path.join(xml_root, name_without_suffix ... | annotation = ET.Element('annotation')
tree = ET.ElementTree(annotation)
folder = ET.Element('folder')
folder.text = '{}'.format(anno.info.folder)
annotation.append(folder)
filename = ET.Element('filename')
filename.text = '{}'.format(anno.info.name)
annot... | annotation = ET.Element('annotation')
tree = ET.ElementTree(annotation)
folder = ET.Element('folder')
folder.text = '{}'.format(anno.info.folder)
annotation.append(folder)
filename = ET.Element('filename')
filename.text = '{}'.format(anno.info.name)
annot... | 222 | 239 | 995 | 1,234 | # -*- coding: utf-8 -*-
# @Author : LG
from ISAT.scripts.isat import ISAT
from xml.etree import ElementTree as ET
import tqdm
import os
class VOCDetect(ISAT):
def __init__(self):
self.keep_crowd = True
def save_to_XML(self, xml_root):
os.makedirs(xml_root, exist_ok=True)
pbar = tqd... | # -*- coding: utf-8 -*-
# @Author : LG
from ISAT.scripts.isat import ISAT
from xml.etree import ElementTree as ET
import tqdm
import os
class VOCDetect(ISAT):
def __init__(self):
self.keep_crowd = True
def save_to_XML(self, xml_root):
os.makedirs(xml_root, exist_ok=True)
pbar = tqd... |
50,552 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/build_sam.py | 20,21 | 1,125 | build_sam_vit_h | 21,30 | 21,30 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from functools import partial
from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWa... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from functools import partial
from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWa... | image_encoder = _build_sam_encoder(
encoder_embed_dim=1280,
encoder_depth=32,
encoder_num_heads=16,
encoder_global_attn_indexes=[7, 15, 23, 31]
)
return _build_sam(image_encoder, checkpoint)
| image_encoder = _build_sam_encoder(
encoder_embed_dim=1280,
encoder_depth=32,
encoder_num_heads=16,
encoder_global_attn_indexes=[7, 15, 23, 31]
)
return _build_sam(image_encoder, checkpoint)
| 14 | 1,188 | 106 | 1,294 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from functools import partial
from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWa... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from functools import partial
from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWa... | |
50,553 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/modeling/common.py | 67,74 | 1,119 | resize | 74,88 | 74,88 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Type
class MLPBlock(nn.Module):
... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Type
class MLPBlock(nn.Module):
... | if mode in ["bilinear", "bicubic"]:
return F.interpolate(
x,
size=size,
scale_factor=scale_factor,
mode=mode,
align_corners=align_corners,
)
elif mode in ["nearest", "area"]:
return F.interpolate(x, size=size, scale_factor=scale... | if mode in ["bilinear", "bicubic"]:
return F.interpolate(
x,
size=size,
scale_factor=scale_factor,
mode=mode,
align_corners=align_corners,
)
elif mode in ["nearest", "area"]:
return F.interpolate(x, size=size, scale_factor=scale... | 28 | 1,190 | 149 | 1,339 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Type
class MLPBlock(nn.Module):
... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Type
class MLPBlock(nn.Module):
... | |
50,554 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/modeling/common.py | 39,40 | 32,47 | forward | LayerNorm2d | 40,47 | 40,47 | class LayerNorm2d(nn.Module):
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
super().__init__()
self.weight = nn.Parameter(torch.ones(num_channels))
self.bias = nn.Parameter(torch.zeros(num_channels))
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.... | class LayerNorm2d(nn.Module):
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
super().__init__()
self.weight = nn.Parameter(torch.ones(num_channels))
self.bias = nn.Parameter(torch.zeros(num_channels))
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.... | u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x
| u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x
| 32 | 135 | 97 | 232 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Type
class MLPBlock(nn.Module):
... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Type
class MLPBlock(nn.Module):
... |
50,555 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/modeling/common.py | 26,27 | 14,32 | forward | MLPBlock | 27,32 | 27,32 | class MLPBlock(nn.Module):
def __init__(
self,
embedding_dim: int,
mlp_dim: int,
act: Type[nn.Module] = nn.GELU,
) -> None:
super().__init__()
self.lin1 = nn.Linear(embedding_dim, mlp_dim)
self.lin2 = nn.Linear(mlp_dim, embedding_dim)
self.act = ac... | class MLPBlock(nn.Module):
def __init__(
self,
embedding_dim: int,
mlp_dim: int,
act: Type[nn.Module] = nn.GELU,
) -> None:
super().__init__()
self.lin1 = nn.Linear(embedding_dim, mlp_dim)
self.lin2 = nn.Linear(mlp_dim, embedding_dim)
self.act = ac... | return self.lin2(self.act(self.lin1(x)))
# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa
| return self.lin2(self.act(self.lin1(x)))
# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa
| 13 | 155 | 138 | 293 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Type
class MLPBlock(nn.Module):
... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Type
class MLPBlock(nn.Module):
... |
50,556 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/modeling/common.py | 115,116 | 106,119 | forward | OpSequential | 116,119 | 116,119 | class OpSequential(nn.Module):
def __init__(self, op_list):
super(OpSequential, self).__init__()
valid_op_list = []
for op in op_list:
if op is not None:
valid_op_list.append(op)
self.op_list = nn.ModuleList(valid_op_list)
def forward(self, x: torch.T... | class OpSequential(nn.Module):
def __init__(self, op_list):
super(OpSequential, self).__init__()
valid_op_list = []
for op in op_list:
if op is not None:
valid_op_list.append(op)
self.op_list = nn.ModuleList(valid_op_list)
def forward(self, x: torch.T... | for op in self.op_list:
x = op(x)
return x | for op in self.op_list:
x = op(x)
return x | 9 | 130 | 25 | 155 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Type
class MLPBlock(nn.Module):
... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Type
class MLPBlock(nn.Module):
... |
50,557 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/modeling/common.py | 102,103 | 88,106 | forward | UpSampleLayer | 103,106 | 103,106 | class UpSampleLayer(nn.Module):
def __init__(
self,
mode="bicubic",
size=None,
factor=2,
align_corners=False,
):
super(UpSampleLayer, self).__init__()
self.mode = mode
self.size = val2list(size, 2) if size is not None else None
... | class UpSampleLayer(nn.Module):
def __init__(
self,
mode="bicubic",
size=None,
factor=2,
align_corners=False,
):
super(UpSampleLayer, self).__init__()
self.mode = mode
self.size = val2list(size, 2) if size is not None else None
... | return resize(x, self.size, self.factor, self.mode, self.align_corners)
| return resize(x, self.size, self.factor, self.mode, self.align_corners)
| 6 | 161 | 31 | 192 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Type
class MLPBlock(nn.Module):
... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Type
class MLPBlock(nn.Module):
... |
50,559 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/modeling/image_encoder.py | 224,225 | 185,243 | forward | Attention | 225,243 | 225,243 | class Attention(nn.Module):
"""Multi-head Attention block with relative position embeddings."""
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = True,
use_rel_pos: bool = False,
rel_pos_zero_init: bool = True,
input_size: Optional[Tuple[... | class Attention(nn.Module):
"""Multi-head Attention block with relative position embeddings."""
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = True,
use_rel_pos: bool = False,
rel_pos_zero_init: bool = True,
input_size: Optional[Tuple[... | B, H, W, _ = x.shape
# qkv with shape (3, B, nHead, H * W, C)
qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
# q, k, v with shape (B * nHead, H * W, C)
q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
attn = (q * self.s... | B, H, W, _ = x.shape
# qkv with shape (3, B, nHead, H * W, C)
qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
# q, k, v with shape (B * nHead, H * W, C)
q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
attn = (q * self.s... | 99 | 516 | 341 | 857 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... |
50,560 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/modeling/image_encoder.py | 166,167 | 119,185 | forward | Block | 167,185 | 167,185 | class Block(nn.Module):
"""Transformer blocks with support of window attention and residual propagation blocks"""
def __init__(
self,
dim: int,
num_heads: int,
mlp_ratio: float = 4.0,
qkv_bias: bool = True,
norm_layer: Type[nn.Module] = nn.LayerNorm,
act_... | class Block(nn.Module):
"""Transformer blocks with support of window attention and residual propagation blocks"""
def __init__(
self,
dim: int,
num_heads: int,
mlp_ratio: float = 4.0,
qkv_bias: bool = True,
norm_layer: Type[nn.Module] = nn.LayerNorm,
act_... | shortcut = x
x = self.norm1(x)
# Window partition
if self.window_size > 0:
H, W = x.shape[1], x.shape[2]
x, pad_hw = window_partition(x, self.window_size)
x = self.attn(x)
# Reverse window partition
if self.window_size > 0:
x =... | shortcut = x
x = self.norm1(x)
# Window partition
if self.window_size > 0:
H, W = x.shape[1], x.shape[2]
x, pad_hw = window_partition(x, self.window_size)
x = self.attn(x)
# Reverse window partition
if self.window_size > 0:
x =... | 53 | 615 | 178 | 793 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... |
50,561 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/modeling/image_encoder.py | 106,107 | 17,119 | forward | ImageEncoderViT | 107,119 | 107,119 | class ImageEncoderViT(nn.Module):
def __init__(
self,
img_size: int = 1024,
patch_size: int = 16,
in_chans: int = 3,
embed_dim: int = 768,
depth: int = 12,
num_heads: int = 12,
mlp_ratio: float = 4.0,
out_chans: int = 256,
qkv_bias: boo... | class ImageEncoderViT(nn.Module):
def __init__(
self,
img_size: int = 1024,
patch_size: int = 16,
in_chans: int = 3,
embed_dim: int = 768,
depth: int = 12,
num_heads: int = 12,
mlp_ratio: float = 4.0,
out_chans: int = 256,
qkv_bias: boo... | x = self.patch_embed(x)
if self.pos_embed is not None:
x = x + self.pos_embed
for blk in self.blocks:
x = blk(x)
x = self.neck(x.permute(0, 3, 1, 2))
return x
| x = self.patch_embed(x)
if self.pos_embed is not None:
x = x + self.pos_embed
for blk in self.blocks:
x = blk(x)
x = self.neck(x.permute(0, 3, 1, 2))
return x
| 28 | 1,041 | 101 | 1,142 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... |
50,562 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/modeling/image_encoder.py | 391,392 | 364,396 | forward | PatchEmbed | 392,396 | 392,396 | class PatchEmbed(nn.Module):
"""
Image to Patch Embedding.
"""
def __init__(
self,
kernel_size: Tuple[int, int] = (16, 16),
stride: Tuple[int, int] = (16, 16),
padding: Tuple[int, int] = (0, 0),
in_chans: int = 3,
embed_dim: int = 768,
) -> None:
... | class PatchEmbed(nn.Module):
"""
Image to Patch Embedding.
"""
def __init__(
self,
kernel_size: Tuple[int, int] = (16, 16),
stride: Tuple[int, int] = (16, 16),
padding: Tuple[int, int] = (0, 0),
in_chans: int = 3,
embed_dim: int = 768,
) -> None:
... | x = self.proj(x)
# B C H W -> B H W C
x = x.permute(0, 2, 3, 1)
return x
| x = self.proj(x)
# B C H W -> B H W C
x = x.permute(0, 2, 3, 1)
return x
| 21 | 301 | 52 | 353 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... |
50,563 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/modeling/mask_decoder.py | 176,177 | 159,182 | forward | MLP | 177,182 | 177,182 | class MLP(nn.Module):
def __init__(
self,
input_dim: int,
hidden_dim: int,
output_dim: int,
num_layers: int,
sigmoid_output: bool = False,
) -> None:
super().__init__()
self.num_layers = num_layers
h = [hidden_dim] * (num_layers - 1)
... | class MLP(nn.Module):
def __init__(
self,
input_dim: int,
hidden_dim: int,
output_dim: int,
num_layers: int,
sigmoid_output: bool = False,
) -> None:
super().__init__()
self.num_layers = num_layers
h = [hidden_dim] * (num_layers - 1)
... | for i, layer in enumerate(self.layers):
x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
if self.sigmoid_output:
x = F.sigmoid(x)
return x
| for i, layer in enumerate(self.layers):
x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
if self.sigmoid_output:
x = F.sigmoid(x)
return x
| 23 | 189 | 75 | 264 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Type
from .common... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Type
from .common... |
50,564 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/modeling/mask_decoder.py | 71,79 | 16,159 | forward | MaskDecoder | 94,117 | 79,117 | class MaskDecoder(nn.Module):
def __init__(
self,
*,
transformer_dim: int,
transformer: nn.Module,
num_multimask_outputs: int = 3,
activation: Type[nn.Module] = nn.GELU,
iou_head_depth: int = 3,
iou_head_hidden_dim: int = 256,
) -> None:
""... | class MaskDecoder(nn.Module):
def __init__(
self,
*,
transformer_dim: int,
transformer: nn.Module,
num_multimask_outputs: int = 3,
activation: Type[nn.Module] = nn.GELU,
iou_head_depth: int = 3,
iou_head_hidden_dim: int = 256,
) -> None:
""... | masks, iou_pred = self.predict_masks(
image_embeddings=image_embeddings,
image_pe=image_pe,
sparse_prompt_embeddings=sparse_prompt_embeddings,
dense_prompt_embeddings=dense_prompt_embeddings,
)
# Select the correct mask or masks for output
... | """
Predict masks given image and prompt embeddings.
Arguments:
image_embeddings (torch.Tensor): the embeddings from the image encoder
image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
sparse_prompt_embeddings (torch.Tensor): the embed... | 61 | 1,610 | 257 | 1,867 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Type
from .common... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Type
from .common... |
50,565 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/modeling/prompt_encoder.py | 196,197 | 182,227 | _pe_encoding | PositionEmbeddingRandom | 198,206 | 197,206 | class PositionEmbeddingRandom(nn.Module):
"""
Positional encoding using random spatial frequencies.
"""
def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
super().__init__()
if scale is None or scale <= 0.0:
scale = 1.0
self.register_... | class PositionEmbeddingRandom(nn.Module):
"""
Positional encoding using random spatial frequencies.
"""
def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
super().__init__()
if scale is None or scale <= 0.0:
scale = 1.0
self.register_... | # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
coords = 2 * coords - 1
coords = coords.to(self.positional_encoding_gaussian_matrix.dtype)
coords = coords @ self.positional_encoding_gaussian_matrix
coords = 2 * np.pi * coords
# outputs d_1 x ..... | """Positionally encode points that are normalized to [0,1]."""
# assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
coords = 2 * coords - 1
coords = coords.to(self.positional_encoding_gaussian_matrix.dtype)
coords = coords @ self.positional_encoding_gaussia... | 54 | 558 | 156 | 714 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from torch import nn
from typing import Any, Optional, Tuple, Type
from .common import L... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from torch import nn
from typing import Any, Optional, Tuple, Type
from .common import L... |
50,568 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/modeling/rep_vit.py | 273,275 | 264,290 | fuse | BN_Linear | 275,290 | 275,290 | class BN_Linear(torch.nn.Sequential):
def __init__(self, a, b, bias=True, std=0.02):
super().__init__()
self.add_module('bn', torch.nn.BatchNorm1d(a))
self.add_module('l', torch.nn.Linear(a, b, bias=bias))
trunc_normal_(self.l.weight, std=std)
if bias:
torch.nn.in... | class BN_Linear(torch.nn.Sequential):
def __init__(self, a, b, bias=True, std=0.02):
super().__init__()
self.add_module('bn', torch.nn.BatchNorm1d(a))
self.add_module('l', torch.nn.Linear(a, b, bias=bias))
trunc_normal_(self.l.weight, std=std)
if bias:
torch.nn.in... | bn, l = self._modules.values()
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
b = bn.bias - self.bn.running_mean * \
self.bn.weight / (bn.running_var + bn.eps) ** 0.5
w = l.weight * w[None, :]
if l.bias is None:
b = b @ self.l.weight.T
else:
... | bn, l = self._modules.values()
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
b = bn.bias - self.bn.running_mean * \
self.bn.weight / (bn.running_var + bn.eps) ** 0.5
w = l.weight * w[None, :]
if l.bias is None:
b = b @ self.l.weight.T
else:
... | 60 | 165 | 227 | 392 | import torch.nn as nn
from .common import LayerNorm2d, UpSampleLayer, OpSequential
__all__ = ['rep_vit_m1', 'rep_vit_m2', 'rep_vit_m3', 'RepViT']
m1_cfgs = [
# k, t, c, SE, HS, s
[3, 2, 48, 1, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 96, 0, 0, 2],
[3, 2, 96, 1, 0, 1],
[3, 2,... | import torch.nn as nn
from .common import LayerNorm2d, UpSampleLayer, OpSequential
__all__ = ['rep_vit_m1', 'rep_vit_m2', 'rep_vit_m3', 'RepViT']
m1_cfgs = [
# k, t, c, SE, HS, s
[3, 2, 48, 1, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 96, 0, 0, 2],
[3, 2, 96, 1, 0, 1],
[3, 2,... |
50,569 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/modeling/rep_vit.py | 138,140 | 128,154 | fuse | Conv2d_BN | 140,154 | 140,154 | class Conv2d_BN(torch.nn.Sequential):
def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1,
groups=1, bn_weight_init=1, resolution=-10000):
super().__init__()
self.add_module('c', torch.nn.Conv2d(
a, b, ks, stride, pad, dilation, groups, bias=False))
self.a... | class Conv2d_BN(torch.nn.Sequential):
def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1,
groups=1, bn_weight_init=1, resolution=-10000):
super().__init__()
self.add_module('c', torch.nn.Conv2d(
a, b, ks, stride, pad, dilation, groups, bias=False))
self.a... | c, bn = self._modules.values()
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
w = c.weight * w[:, None, None, None]
b = bn.bias - bn.running_mean * bn.weight / \
(bn.running_var + bn.eps) ** 0.5
m = torch.nn.Conv2d(w.size(1) * self.c.groups, w.size(
0), ... | c, bn = self._modules.values()
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
w = c.weight * w[:, None, None, None]
b = bn.bias - bn.running_mean * bn.weight / \
(bn.running_var + bn.eps) ** 0.5
m = torch.nn.Conv2d(w.size(1) * self.c.groups, w.size(
0), ... | 52 | 219 | 233 | 452 | import torch.nn as nn
from .common import LayerNorm2d, UpSampleLayer, OpSequential
__all__ = ['rep_vit_m1', 'rep_vit_m2', 'rep_vit_m3', 'RepViT']
m1_cfgs = [
# k, t, c, SE, HS, s
[3, 2, 48, 1, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 96, 0, 0, 2],
[3, 2, 96, 1, 0, 1],
[3, 2,... | import torch.nn as nn
from .common import LayerNorm2d, UpSampleLayer, OpSequential
__all__ = ['rep_vit_m1', 'rep_vit_m2', 'rep_vit_m3', 'RepViT']
m1_cfgs = [
# k, t, c, SE, HS, s
[3, 2, 48, 1, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 96, 0, 0, 2],
[3, 2, 96, 1, 0, 1],
[3, 2,... |
50,570 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/modeling/rep_vit.py | 197,199 | 187,220 | fuse | RepVGGDW | 199,220 | 199,220 | class RepVGGDW(torch.nn.Module):
def __init__(self, ed) -> None:
super().__init__()
self.conv = Conv2d_BN(ed, ed, 3, 1, 1, groups=ed)
self.conv1 = Conv2d_BN(ed, ed, 1, 1, 0, groups=ed)
self.dim = ed
def forward(self, x):
return self.conv(x) + self.conv1(x) + x
@torc... | class RepVGGDW(torch.nn.Module):
def __init__(self, ed) -> None:
super().__init__()
self.conv = Conv2d_BN(ed, ed, 3, 1, 1, groups=ed)
self.conv1 = Conv2d_BN(ed, ed, 1, 1, 0, groups=ed)
self.dim = ed
def forward(self, x):
return self.conv(x) + self.conv1(x) + x
@torc... | conv = self.conv.fuse()
conv1 = self.conv1.fuse()
conv_w = conv.weight
conv_b = conv.bias
conv1_w = conv1.weight
conv1_b = conv1.bias
conv1_w = torch.nn.functional.pad(conv1_w, [1, 1, 1, 1])
identity = torch.nn.functional.pad(torch.ones(conv1_w.shape[0]... | conv = self.conv.fuse()
conv1 = self.conv1.fuse()
conv_w = conv.weight
conv_b = conv.bias
conv1_w = conv1.weight
conv1_b = conv1.bias
conv1_w = torch.nn.functional.pad(conv1_w, [1, 1, 1, 1])
identity = torch.nn.functional.pad(torch.ones(conv1_w.shape[0]... | 52 | 161 | 266 | 427 | import torch.nn as nn
from .common import LayerNorm2d, UpSampleLayer, OpSequential
__all__ = ['rep_vit_m1', 'rep_vit_m2', 'rep_vit_m3', 'RepViT']
m1_cfgs = [
# k, t, c, SE, HS, s
[3, 2, 48, 1, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 96, 0, 0, 2],
[3, 2, 96, 1, 0, 1],
[3, 2,... | import torch.nn as nn
from .common import LayerNorm2d, UpSampleLayer, OpSequential
__all__ = ['rep_vit_m1', 'rep_vit_m2', 'rep_vit_m3', 'RepViT']
m1_cfgs = [
# k, t, c, SE, HS, s
[3, 2, 48, 1, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 96, 0, 0, 2],
[3, 2, 96, 1, 0, 1],
[3, 2,... |
50,571 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/modeling/rep_vit.py | 339,340 | 290,358 | forward | RepViT | 340,358 | 340,358 | class RepViT(nn.Module):
arch_settings = {
'm1': m1_cfgs,
'm2': m2_cfgs,
'm3': m3_cfgs
}
def __init__(self, arch, img_size=1024, upsample_mode='bicubic'):
super(RepViT, self).__init__()
# setting of inverted residual blocks
self.cfgs = self.arch_settings[arch... | class RepViT(nn.Module):
arch_settings = {
'm1': m1_cfgs,
'm2': m2_cfgs,
'm3': m3_cfgs
}
def __init__(self, arch, img_size=1024, upsample_mode='bicubic'):
super(RepViT, self).__init__()
# setting of inverted residual blocks
self.cfgs = self.arch_settings[arch... | counter = 0
output_dict = dict()
# patch_embed
x = self.features[0](x)
output_dict['stem'] = x
# stages
for idx, f in enumerate(self.features[1:]):
x = f(x)
if idx in self.stage_idx:
output_dict[f'stage{counter}'] = x
... | counter = 0
output_dict = dict()
# patch_embed
x = self.features[0](x)
output_dict['stem'] = x
# stages
for idx, f in enumerate(self.features[1:]):
x = f(x)
if idx in self.stage_idx:
output_dict[f'stage{counter}'] = x
... | 44 | 760 | 174 | 934 | import torch.nn as nn
from .common import LayerNorm2d, UpSampleLayer, OpSequential
__all__ = ['rep_vit_m1', 'rep_vit_m2', 'rep_vit_m3', 'RepViT']
m1_cfgs = [
# k, t, c, SE, HS, s
[3, 2, 48, 1, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 96, 0, 0, 2],
[3, 2, 96, 1, 0, 1],
[3, 2,... | import torch.nn as nn
from .common import LayerNorm2d, UpSampleLayer, OpSequential
__all__ = ['rep_vit_m1', 'rep_vit_m2', 'rep_vit_m3', 'RepViT']
m1_cfgs = [
# k, t, c, SE, HS, s
[3, 2, 48, 1, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 96, 0, 0, 2],
[3, 2, 96, 1, 0, 1],
[3, 2,... |
50,572 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/modeling/rep_vit.py | 257,258 | 220,261 | forward | RepViTBlock | 258,261 | 258,261 | class RepViTBlock(nn.Module):
def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se, use_hs, skip_downsample=False):
super(RepViTBlock, self).__init__()
assert stride in [1, 2]
self.identity = stride == 1 and inp == oup
assert (hidden_dim == 2 * inp)
if strid... | class RepViTBlock(nn.Module):
def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se, use_hs, skip_downsample=False):
super(RepViTBlock, self).__init__()
assert stride in [1, 2]
self.identity = stride == 1 and inp == oup
assert (hidden_dim == 2 * inp)
if strid... | return self.channel_mixer(self.token_mixer(x))
| return self.channel_mixer(self.token_mixer(x))
| 2 | 568 | 24 | 592 | import torch.nn as nn
from .common import LayerNorm2d, UpSampleLayer, OpSequential
__all__ = ['rep_vit_m1', 'rep_vit_m2', 'rep_vit_m3', 'RepViT']
m1_cfgs = [
# k, t, c, SE, HS, s
[3, 2, 48, 1, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 96, 0, 0, 2],
[3, 2, 96, 1, 0, 1],
[3, 2,... | import torch.nn as nn
from .common import LayerNorm2d, UpSampleLayer, OpSequential
__all__ = ['rep_vit_m1', 'rep_vit_m2', 'rep_vit_m3', 'RepViT']
m1_cfgs = [
# k, t, c, SE, HS, s
[3, 2, 48, 1, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 96, 0, 0, 2],
[3, 2, 96, 1, 0, 1],
[3, 2,... |
50,573 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/modeling/rep_vit.py | 167,169 | 154,187 | fuse | Residual | 169,187 | 169,187 | class Residual(torch.nn.Module):
def __init__(self, m, drop=0.):
super().__init__()
self.m = m
self.drop = drop
def forward(self, x):
if self.training and self.drop > 0:
return x + self.m(x) * torch.rand(x.size(0), 1, 1, 1,
... | class Residual(torch.nn.Module):
def __init__(self, m, drop=0.):
super().__init__()
self.m = m
self.drop = drop
def forward(self, x):
if self.training and self.drop > 0:
return x + self.m(x) * torch.rand(x.size(0), 1, 1, 1,
... | if isinstance(self.m, Conv2d_BN):
m = self.m.fuse()
assert (m.groups == m.in_channels)
identity = torch.ones(m.weight.shape[0], m.weight.shape[1], 1, 1)
identity = torch.nn.functional.pad(identity, [1, 1, 1, 1])
m.weight += identity.to(m.weight.device)... | if isinstance(self.m, Conv2d_BN):
m = self.m.fuse()
assert (m.groups == m.in_channels)
identity = torch.ones(m.weight.shape[0], m.weight.shape[1], 1, 1)
identity = torch.nn.functional.pad(identity, [1, 1, 1, 1])
m.weight += identity.to(m.weight.device)... | 59 | 173 | 272 | 445 | import torch.nn as nn
from .common import LayerNorm2d, UpSampleLayer, OpSequential
__all__ = ['rep_vit_m1', 'rep_vit_m2', 'rep_vit_m3', 'RepViT']
m1_cfgs = [
# k, t, c, SE, HS, s
[3, 2, 48, 1, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 96, 0, 0, 2],
[3, 2, 96, 1, 0, 1],
[3, 2,... | import torch.nn as nn
from .common import LayerNorm2d, UpSampleLayer, OpSequential
__all__ = ['rep_vit_m1', 'rep_vit_m2', 'rep_vit_m3', 'RepViT']
m1_cfgs = [
# k, t, c, SE, HS, s
[3, 2, 48, 1, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 96, 0, 0, 2],
[3, 2, 96, 1, 0, 1],
[3, 2,... |
50,574 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/modeling/transformer.py | 208,209 | 185,241 | _separate_heads | Attention | 209,213 | 209,213 | class Attention(nn.Module):
"""
An attention layer that allows for downscaling the size of the embedding
after projection to queries, keys, and values.
"""
def __init__(
self,
embedding_dim: int,
num_heads: int,
downsample_rate: int = 1,
) -> None:
super(... | class Attention(nn.Module):
"""
An attention layer that allows for downscaling the size of the embedding
after projection to queries, keys, and values.
"""
def __init__(
self,
embedding_dim: int,
num_heads: int,
downsample_rate: int = 1,
) -> None:
super(... | b, n, c = x.shape
x = x.reshape(b, n, num_heads, c // num_heads)
return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head
| b, n, c = x.shape
x = x.reshape(b, n, num_heads, c // num_heads)
return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head
| 24 | 684 | 73 | 757 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import Tensor, nn
import math
from typing import Tuple, Type
from .common import MLPBlock
clas... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import Tensor, nn
import math
from typing import Tuple, Type
from .common import MLPBlock
clas... |
50,575 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/modeling/transformer.py | 151,154 | 109,185 | forward | TwoWayAttentionBlock | 154,185 | 154,185 | class TwoWayAttentionBlock(nn.Module):
def __init__(
self,
embedding_dim: int,
num_heads: int,
mlp_dim: int = 2048,
activation: Type[nn.Module] = nn.ReLU,
attention_downsample_rate: int = 2,
skip_first_layer_pe: bool = False,
) -> None:
"""
... | class TwoWayAttentionBlock(nn.Module):
def __init__(
self,
embedding_dim: int,
num_heads: int,
mlp_dim: int = 2048,
activation: Type[nn.Module] = nn.ReLU,
attention_downsample_rate: int = 2,
skip_first_layer_pe: bool = False,
) -> None:
"""
... | # Self attention block
if self.skip_first_layer_pe:
queries = self.self_attn(q=queries, k=queries, v=queries)
else:
q = queries + query_pe
attn_out = self.self_attn(q=q, k=q, v=queries)
queries = queries + attn_out
queries = self.norm1(quer... | # Self attention block
if self.skip_first_layer_pe:
queries = self.self_attn(q=queries, k=queries, v=queries)
else:
q = queries + query_pe
attn_out = self.self_attn(q=q, k=q, v=queries)
queries = queries + attn_out
queries = self.norm1(quer... | 111 | 552 | 361 | 913 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import Tensor, nn
import math
from typing import Tuple, Type
from .common import MLPBlock
clas... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import Tensor, nn
import math
from typing import Tuple, Type
from .common import MLPBlock
clas... |
50,576 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/modeling/transformer.py | 62,68 | 16,109 | forward | TwoWayTransformer | 81,109 | 68,109 | class TwoWayTransformer(nn.Module):
def __init__(
self,
depth: int,
embedding_dim: int,
num_heads: int,
mlp_dim: int,
activation: Type[nn.Module] = nn.ReLU,
attention_downsample_rate: int = 2,
) -> None:
"""
A transformer decoder that atten... | class TwoWayTransformer(nn.Module):
def __init__(
self,
depth: int,
embedding_dim: int,
num_heads: int,
mlp_dim: int,
activation: Type[nn.Module] = nn.ReLU,
attention_downsample_rate: int = 2,
) -> None:
"""
A transformer decoder that atten... | # BxCxHxW -> BxHWxC == B x N_image_tokens x C
bs, c, h, w = image_embedding.shape
image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
image_pe = image_pe.flatten(2).permute(0, 2, 1)
# Prepare queries
queries = point_embedding
keys = image_embedding
... | """
Args:
image_embedding (torch.Tensor): image to attend to. Should be shape
B x embedding_dim x h x w for any h and w.
image_pe (torch.Tensor): the positional encoding to add to the image. Must
have the same shape as image_embedding.
point_embeddin... | 93 | 675 | 320 | 995 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import Tensor, nn
import math
from typing import Tuple, Type
from .common import MLPBlock
clas... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import Tensor, nn
import math
from typing import Tuple, Type
from .common import MLPBlock
clas... |
50,578 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/utils/coreml.py | 42,49 | 7,65 | forward | SamCoreMLModel | 49,65 | 49,65 | class SamCoreMLModel(nn.Module):
"""
This model should not be called directly, but is used in CoreML export.
"""
def __init__(
self,
model: Sam,
use_stability_score: bool = False
) -> None:
super().__init__()
self.mask_decoder = model.mask_decoder
sel... | class SamCoreMLModel(nn.Module):
"""
This model should not be called directly, but is used in CoreML export.
"""
def __init__(
self,
model: Sam,
use_stability_score: bool = False
) -> None:
super().__init__()
self.mask_decoder = model.mask_decoder
sel... | sparse_embedding = self._embed_points(point_coords, point_labels)
dense_embedding = self.model.prompt_encoder.no_mask_embed.weight.reshape(1, -1, 1, 1)
masks, scores = self.model.mask_decoder.predict_masks(
image_embeddings=image_embeddings,
image_pe=self.model.prompt_en... | sparse_embedding = self._embed_points(point_coords, point_labels)
dense_embedding = self.model.prompt_encoder.no_mask_embed.weight.reshape(1, -1, 1, 1)
masks, scores = self.model.mask_decoder.predict_masks(
image_embeddings=image_embeddings,
image_pe=self.model.prompt_en... | 31 | 492 | 226 | 718 | import torch
import torch.nn as nn
from ..modeling import Sam
from .amg import calculate_stability_score
class SamCoreMLModel(nn.Module):
"""
This model should not be called directly, but is used in CoreML export.
"""
def __init__(
self,
model: Sam,
use_stability_score: bool =... | import torch
import torch.nn as nn
from ..modeling import Sam
from .amg import calculate_stability_score
class SamCoreMLModel(nn.Module):
"""
This model should not be called directly, but is used in CoreML export.
"""
def __init__(
self,
model: Sam,
use_stability_score: bool =... |
50,579 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/edge_sam/utils/transforms.py | 93,95 | 16,103 | get_preprocess_shape | ResizeLongestSide | 98,103 | 95,103 | class ResizeLongestSide:
"""
Resizes images to the longest side 'target_length', as well as provides
methods for resizing coordinates and boxes. Provides methods for
transforming both numpy array and batched torch tensors.
"""
def __init__(self, target_length: int) -> None:
self.target_... | class ResizeLongestSide:
"""
Resizes images to the longest side 'target_length', as well as provides
methods for resizing coordinates and boxes. Provides methods for
transforming both numpy array and batched torch tensors.
"""
def __init__(self, target_length: int) -> None:
self.target_... | scale = long_side_length * 1.0 / max(oldh, oldw)
newh, neww = oldh * scale, oldw * scale
neww = int(neww + 0.5)
newh = int(newh + 0.5)
return (newh, neww)
| """
Compute the output size given input size and target long side length.
"""
scale = long_side_length * 1.0 / max(oldh, oldw)
newh, neww = oldh * scale, oldw * scale
neww = int(neww + 0.5)
newh = int(newh + 0.5)
return (newh, neww)
| 30 | 1,123 | 83 | 1,206 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from torch.nn import functional as F
from torchvision.transforms.functional import resize,... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from torch.nn import functional as F
from torchvision.transforms.functional import resize,... |
50,581 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/mobile_sam/build_sam.py | 47,48 | 1,160 | build_sam_vit_t | 48,96 | 48,96 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from functools import partial
from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWa... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from functools import partial
from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWa... | prompt_embed_dim = 256
image_size = 1024
vit_patch_size = 16
image_embedding_size = image_size // vit_patch_size
mobile_sam = Sam(
image_encoder=TinyViT(img_size=1024, in_chans=3, num_classes=1000,
embed_dims=[64, 128, 160, 320],
depths=[2, 2, 6, 2],
... | prompt_embed_dim = 256
image_size = 1024
vit_patch_size = 16
image_embedding_size = image_size // vit_patch_size
mobile_sam = Sam(
image_encoder=TinyViT(img_size=1024, in_chans=3, num_classes=1000,
embed_dims=[64, 128, 160, 320],
depths=[2, 2, 6, 2],
... | 88 | 1,137 | 635 | 1,772 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from functools import partial
from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWa... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from functools import partial
from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWa... | |
50,582 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/mobile_sam/modeling/common.py | 38,39 | 31,44 | forward | LayerNorm2d | 39,44 | 39,44 | class LayerNorm2d(nn.Module):
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
super().__init__()
self.weight = nn.Parameter(torch.ones(num_channels))
self.bias = nn.Parameter(torch.zeros(num_channels))
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.... | class LayerNorm2d(nn.Module):
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
super().__init__()
self.weight = nn.Parameter(torch.ones(num_channels))
self.bias = nn.Parameter(torch.zeros(num_channels))
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.... | u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x
| u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x
| 32 | 135 | 93 | 228 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from typing import Type
class MLPBlock(nn.Module):
def __init__(
self,
... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from typing import Type
class MLPBlock(nn.Module):
def __init__(
self,
... |
50,583 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/mobile_sam/modeling/common.py | 25,26 | 13,31 | forward | MLPBlock | 26,31 | 26,31 | class MLPBlock(nn.Module):
def __init__(
self,
embedding_dim: int,
mlp_dim: int,
act: Type[nn.Module] = nn.GELU,
) -> None:
super().__init__()
self.lin1 = nn.Linear(embedding_dim, mlp_dim)
self.lin2 = nn.Linear(mlp_dim, embedding_dim)
self.act = ac... | class MLPBlock(nn.Module):
def __init__(
self,
embedding_dim: int,
mlp_dim: int,
act: Type[nn.Module] = nn.GELU,
) -> None:
super().__init__()
self.lin1 = nn.Linear(embedding_dim, mlp_dim)
self.lin2 = nn.Linear(mlp_dim, embedding_dim)
self.act = ac... | return self.lin2(self.act(self.lin1(x)))
# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa
| return self.lin2(self.act(self.lin1(x)))
# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa
| 13 | 155 | 138 | 293 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from typing import Type
class MLPBlock(nn.Module):
def __init__(
self,
... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from typing import Type
class MLPBlock(nn.Module):
def __init__(
self,
... |
50,585 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/mobile_sam/modeling/image_encoder.py | 224,225 | 185,243 | forward | Attention | 225,243 | 225,243 | class Attention(nn.Module):
"""Multi-head Attention block with relative position embeddings."""
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = True,
use_rel_pos: bool = False,
rel_pos_zero_init: bool = True,
input_size: Optional[Tuple[... | class Attention(nn.Module):
"""Multi-head Attention block with relative position embeddings."""
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = True,
use_rel_pos: bool = False,
rel_pos_zero_init: bool = True,
input_size: Optional[Tuple[... | B, H, W, _ = x.shape
# qkv with shape (3, B, nHead, H * W, C)
qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
# q, k, v with shape (B * nHead, H * W, C)
q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
attn = (q * self.s... | B, H, W, _ = x.shape
# qkv with shape (3, B, nHead, H * W, C)
qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
# q, k, v with shape (B * nHead, H * W, C)
q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
attn = (q * self.s... | 99 | 516 | 341 | 857 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... |
50,586 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/mobile_sam/modeling/image_encoder.py | 166,167 | 119,185 | forward | Block | 167,185 | 167,185 | class Block(nn.Module):
"""Transformer blocks with support of window attention and residual propagation blocks"""
def __init__(
self,
dim: int,
num_heads: int,
mlp_ratio: float = 4.0,
qkv_bias: bool = True,
norm_layer: Type[nn.Module] = nn.LayerNorm,
act_... | class Block(nn.Module):
"""Transformer blocks with support of window attention and residual propagation blocks"""
def __init__(
self,
dim: int,
num_heads: int,
mlp_ratio: float = 4.0,
qkv_bias: bool = True,
norm_layer: Type[nn.Module] = nn.LayerNorm,
act_... | shortcut = x
x = self.norm1(x)
# Window partition
if self.window_size > 0:
H, W = x.shape[1], x.shape[2]
x, pad_hw = window_partition(x, self.window_size)
x = self.attn(x)
# Reverse window partition
if self.window_size > 0:
x =... | shortcut = x
x = self.norm1(x)
# Window partition
if self.window_size > 0:
H, W = x.shape[1], x.shape[2]
x, pad_hw = window_partition(x, self.window_size)
x = self.attn(x)
# Reverse window partition
if self.window_size > 0:
x =... | 53 | 615 | 178 | 793 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... |
50,587 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/mobile_sam/modeling/image_encoder.py | 106,107 | 17,119 | forward | ImageEncoderViT | 107,119 | 107,119 | class ImageEncoderViT(nn.Module):
def __init__(
self,
img_size: int = 1024,
patch_size: int = 16,
in_chans: int = 3,
embed_dim: int = 768,
depth: int = 12,
num_heads: int = 12,
mlp_ratio: float = 4.0,
out_chans: int = 256,
qkv_bias: boo... | class ImageEncoderViT(nn.Module):
def __init__(
self,
img_size: int = 1024,
patch_size: int = 16,
in_chans: int = 3,
embed_dim: int = 768,
depth: int = 12,
num_heads: int = 12,
mlp_ratio: float = 4.0,
out_chans: int = 256,
qkv_bias: boo... | x = self.patch_embed(x)
if self.pos_embed is not None:
x = x + self.pos_embed
for blk in self.blocks:
x = blk(x)
x = self.neck(x.permute(0, 3, 1, 2))
return x
| x = self.patch_embed(x)
if self.pos_embed is not None:
x = x + self.pos_embed
for blk in self.blocks:
x = blk(x)
x = self.neck(x.permute(0, 3, 1, 2))
return x
| 28 | 1,041 | 101 | 1,142 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... |
50,588 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/mobile_sam/modeling/image_encoder.py | 391,392 | 364,396 | forward | PatchEmbed | 392,396 | 392,396 | class PatchEmbed(nn.Module):
"""
Image to Patch Embedding.
"""
def __init__(
self,
kernel_size: Tuple[int, int] = (16, 16),
stride: Tuple[int, int] = (16, 16),
padding: Tuple[int, int] = (0, 0),
in_chans: int = 3,
embed_dim: int = 768,
) -> None:
... | class PatchEmbed(nn.Module):
"""
Image to Patch Embedding.
"""
def __init__(
self,
kernel_size: Tuple[int, int] = (16, 16),
stride: Tuple[int, int] = (16, 16),
padding: Tuple[int, int] = (0, 0),
in_chans: int = 3,
embed_dim: int = 768,
) -> None:
... | x = self.proj(x)
# B C H W -> B H W C
x = x.permute(0, 2, 3, 1)
return x
| x = self.proj(x)
# B C H W -> B H W C
x = x.permute(0, 2, 3, 1)
return x
| 21 | 301 | 52 | 353 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... |
50,589 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/mobile_sam/modeling/mask_decoder.py | 171,172 | 154,177 | forward | MLP | 172,177 | 172,177 | class MLP(nn.Module):
def __init__(
self,
input_dim: int,
hidden_dim: int,
output_dim: int,
num_layers: int,
sigmoid_output: bool = False,
) -> None:
super().__init__()
self.num_layers = num_layers
h = [hidden_dim] * (num_layers - 1)
... | class MLP(nn.Module):
def __init__(
self,
input_dim: int,
hidden_dim: int,
output_dim: int,
num_layers: int,
sigmoid_output: bool = False,
) -> None:
super().__init__()
self.num_layers = num_layers
h = [hidden_dim] * (num_layers - 1)
... | for i, layer in enumerate(self.layers):
x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
if self.sigmoid_output:
x = F.sigmoid(x)
return x
| for i, layer in enumerate(self.layers):
x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
if self.sigmoid_output:
x = F.sigmoid(x)
return x
| 23 | 189 | 75 | 264 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Type
from .common... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Type
from .common... |
50,590 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/mobile_sam/modeling/mask_decoder.py | 112,119 | 16,154 | predict_masks | MaskDecoder | 120,154 | 119,154 | class MaskDecoder(nn.Module):
def __init__(
self,
*,
transformer_dim: int,
transformer: nn.Module,
num_multimask_outputs: int = 3,
activation: Type[nn.Module] = nn.GELU,
iou_head_depth: int = 3,
iou_head_hidden_dim: int = 256,
) -> None:
""... | class MaskDecoder(nn.Module):
def __init__(
self,
*,
transformer_dim: int,
transformer: nn.Module,
num_multimask_outputs: int = 3,
activation: Type[nn.Module] = nn.GELU,
iou_head_depth: int = 3,
iou_head_hidden_dim: int = 256,
) -> None:
""... | # Concatenate output tokens
output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)
tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
# Expan... | """Predicts masks. See 'forward' for more details."""
# Concatenate output tokens
output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)
tokens = torch.cat((out... | 147 | 1,201 | 617 | 1,818 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Type
from .common... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Type
from .common... |
50,591 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/mobile_sam/modeling/prompt_encoder.py | 206,207 | 182,227 | forward | PositionEmbeddingRandom | 208,219 | 207,219 | class PositionEmbeddingRandom(nn.Module):
"""
Positional encoding using random spatial frequencies.
"""
def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
super().__init__()
if scale is None or scale <= 0.0:
scale = 1.0
self.register_... | class PositionEmbeddingRandom(nn.Module):
"""
Positional encoding using random spatial frequencies.
"""
def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
super().__init__()
if scale is None or scale <= 0.0:
scale = 1.0
self.register_... | h, w = size
device: Any = self.positional_encoding_gaussian_matrix.device
grid = torch.ones((h, w), device=device, dtype=torch.float32)
y_embed = grid.cumsum(dim=0) - 0.5
x_embed = grid.cumsum(dim=1) - 0.5
y_embed = y_embed / h
x_embed = x_embed / w
pe = ... | """Generate positional encoding for a grid of the specified size."""
h, w = size
device: Any = self.positional_encoding_gaussian_matrix.device
grid = torch.ones((h, w), device=device, dtype=torch.float32)
y_embed = grid.cumsum(dim=0) - 0.5
x_embed = grid.cumsum(dim=1) - 0... | 49 | 535 | 182 | 717 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from torch import nn
from typing import Any, Optional, Tuple, Type
from .common import L... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from torch import nn
from typing import Any, Optional, Tuple, Type
from .common import L... |
50,593 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/mobile_sam/modeling/transformer.py | 208,209 | 185,241 | _separate_heads | Attention | 209,213 | 209,213 | class Attention(nn.Module):
"""
An attention layer that allows for downscaling the size of the embedding
after projection to queries, keys, and values.
"""
def __init__(
self,
embedding_dim: int,
num_heads: int,
downsample_rate: int = 1,
) -> None:
super(... | class Attention(nn.Module):
"""
An attention layer that allows for downscaling the size of the embedding
after projection to queries, keys, and values.
"""
def __init__(
self,
embedding_dim: int,
num_heads: int,
downsample_rate: int = 1,
) -> None:
super(... | b, n, c = x.shape
x = x.reshape(b, n, num_heads, c // num_heads)
return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head
| b, n, c = x.shape
x = x.reshape(b, n, num_heads, c // num_heads)
return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head
| 24 | 684 | 73 | 757 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import Tensor, nn
import math
from typing import Tuple, Type
from .common import MLPBlock
clas... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import Tensor, nn
import math
from typing import Tuple, Type
from .common import MLPBlock
clas... |
50,594 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/mobile_sam/modeling/transformer.py | 151,154 | 109,185 | forward | TwoWayAttentionBlock | 154,185 | 154,185 | class TwoWayAttentionBlock(nn.Module):
def __init__(
self,
embedding_dim: int,
num_heads: int,
mlp_dim: int = 2048,
activation: Type[nn.Module] = nn.ReLU,
attention_downsample_rate: int = 2,
skip_first_layer_pe: bool = False,
) -> None:
"""
... | class TwoWayAttentionBlock(nn.Module):
def __init__(
self,
embedding_dim: int,
num_heads: int,
mlp_dim: int = 2048,
activation: Type[nn.Module] = nn.ReLU,
attention_downsample_rate: int = 2,
skip_first_layer_pe: bool = False,
) -> None:
"""
... | # Self attention block
if self.skip_first_layer_pe:
queries = self.self_attn(q=queries, k=queries, v=queries)
else:
q = queries + query_pe
attn_out = self.self_attn(q=q, k=q, v=queries)
queries = queries + attn_out
queries = self.norm1(quer... | # Self attention block
if self.skip_first_layer_pe:
queries = self.self_attn(q=queries, k=queries, v=queries)
else:
q = queries + query_pe
attn_out = self.self_attn(q=q, k=q, v=queries)
queries = queries + attn_out
queries = self.norm1(quer... | 111 | 552 | 361 | 913 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import Tensor, nn
import math
from typing import Tuple, Type
from .common import MLPBlock
clas... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import Tensor, nn
import math
from typing import Tuple, Type
from .common import MLPBlock
clas... |
50,595 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/mobile_sam/modeling/transformer.py | 62,68 | 16,109 | forward | TwoWayTransformer | 81,109 | 68,109 | class TwoWayTransformer(nn.Module):
def __init__(
self,
depth: int,
embedding_dim: int,
num_heads: int,
mlp_dim: int,
activation: Type[nn.Module] = nn.ReLU,
attention_downsample_rate: int = 2,
) -> None:
"""
A transformer decoder that atten... | class TwoWayTransformer(nn.Module):
def __init__(
self,
depth: int,
embedding_dim: int,
num_heads: int,
mlp_dim: int,
activation: Type[nn.Module] = nn.ReLU,
attention_downsample_rate: int = 2,
) -> None:
"""
A transformer decoder that atten... | # BxCxHxW -> BxHWxC == B x N_image_tokens x C
bs, c, h, w = image_embedding.shape
image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
image_pe = image_pe.flatten(2).permute(0, 2, 1)
# Prepare queries
queries = point_embedding
keys = image_embedding
... | """
Args:
image_embedding (torch.Tensor): image to attend to. Should be shape
B x embedding_dim x h x w for any h and w.
image_pe (torch.Tensor): the positional encoding to add to the image. Must
have the same shape as image_embedding.
point_embeddin... | 93 | 675 | 320 | 995 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import Tensor, nn
import math
from typing import Tuple, Type
from .common import MLPBlock
clas... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import Tensor, nn
import math
from typing import Tuple, Type
from .common import MLPBlock
clas... |
50,597 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/mobile_sam/utils/transforms.py | 67,70 | 16,103 | apply_coords_torch | ResizeLongestSide | 74,83 | 70,83 | class ResizeLongestSide:
"""
Resizes images to the longest side 'target_length', as well as provides
methods for resizing coordinates and boxes. Provides methods for
transforming both numpy array and batched torch tensors.
"""
def __init__(self, target_length: int) -> None:
self.target_... | class ResizeLongestSide:
"""
Resizes images to the longest side 'target_length', as well as provides
methods for resizing coordinates and boxes. Provides methods for
transforming both numpy array and batched torch tensors.
"""
def __init__(self, target_length: int) -> None:
self.target_... | old_h, old_w = original_size
new_h, new_w = self.get_preprocess_shape(
original_size[0], original_size[1], self.target_length
)
coords = deepcopy(coords).to(torch.float)
coords[..., 0] = coords[..., 0] * (new_w / old_w)
coords[..., 1] = coords[..., 1] * (new_h... | """
Expects a torch tensor with length 2 in the last dimension. Requires the
original image size in (H, W) format.
"""
old_h, old_w = original_size
new_h, new_w = self.get_preprocess_shape(
original_size[0], original_size[1], self.target_length
)
... | 35 | 1,065 | 145 | 1,210 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from torch.nn import functional as F
from torchvision.transforms.functional import resize,... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from torch.nn import functional as F
from torchvision.transforms.functional import resize,... |
50,599 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/segment_anything/build_sam.py | 14,15 | 1,108 | build_sam_vit_h | 15,24 | 15,24 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from functools import partial
from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWa... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from functools import partial
from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWa... | return _build_sam(
encoder_embed_dim=1280,
encoder_depth=32,
encoder_num_heads=16,
encoder_global_attn_indexes=[7, 15, 23, 31],
checkpoint=checkpoint,
)
| return _build_sam(
encoder_embed_dim=1280,
encoder_depth=32,
encoder_num_heads=16,
encoder_global_attn_indexes=[7, 15, 23, 31],
checkpoint=checkpoint,
)
| 11 | 1,014 | 91 | 1,105 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from functools import partial
from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWa... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from functools import partial
from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWa... | |
50,600 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/segment_anything/modeling/common.py | 38,39 | 31,44 | forward | LayerNorm2d | 39,44 | 39,44 | class LayerNorm2d(nn.Module):
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
super().__init__()
self.weight = nn.Parameter(torch.ones(num_channels))
self.bias = nn.Parameter(torch.zeros(num_channels))
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.... | class LayerNorm2d(nn.Module):
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
super().__init__()
self.weight = nn.Parameter(torch.ones(num_channels))
self.bias = nn.Parameter(torch.zeros(num_channels))
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.... | u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x
| u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x
| 32 | 135 | 93 | 228 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from typing import Type
class MLPBlock(nn.Module):
def __init__(
self,
... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from typing import Type
class MLPBlock(nn.Module):
def __init__(
self,
... |
50,601 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/segment_anything/modeling/common.py | 25,26 | 13,31 | forward | MLPBlock | 26,31 | 26,31 | class MLPBlock(nn.Module):
def __init__(
self,
embedding_dim: int,
mlp_dim: int,
act: Type[nn.Module] = nn.GELU,
) -> None:
super().__init__()
self.lin1 = nn.Linear(embedding_dim, mlp_dim)
self.lin2 = nn.Linear(mlp_dim, embedding_dim)
self.act = ac... | class MLPBlock(nn.Module):
def __init__(
self,
embedding_dim: int,
mlp_dim: int,
act: Type[nn.Module] = nn.GELU,
) -> None:
super().__init__()
self.lin1 = nn.Linear(embedding_dim, mlp_dim)
self.lin2 = nn.Linear(mlp_dim, embedding_dim)
self.act = ac... | return self.lin2(self.act(self.lin1(x)))
# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa
| return self.lin2(self.act(self.lin1(x)))
# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa
| 13 | 155 | 138 | 293 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from typing import Type
class MLPBlock(nn.Module):
def __init__(
self,
... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from typing import Type
class MLPBlock(nn.Module):
def __init__(
self,
... |
50,603 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/segment_anything/modeling/image_encoder.py | 224,225 | 185,243 | forward | Attention | 225,243 | 225,243 | class Attention(nn.Module):
"""Multi-head Attention block with relative position embeddings."""
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = True,
use_rel_pos: bool = False,
rel_pos_zero_init: bool = True,
input_size: Optional[Tuple[... | class Attention(nn.Module):
"""Multi-head Attention block with relative position embeddings."""
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = True,
use_rel_pos: bool = False,
rel_pos_zero_init: bool = True,
input_size: Optional[Tuple[... | B, H, W, _ = x.shape
# qkv with shape (3, B, nHead, H * W, C)
qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
# q, k, v with shape (B * nHead, H * W, C)
q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
attn = (q * self.s... | B, H, W, _ = x.shape
# qkv with shape (3, B, nHead, H * W, C)
qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
# q, k, v with shape (B * nHead, H * W, C)
q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
attn = (q * self.s... | 99 | 516 | 341 | 857 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... |
50,604 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/segment_anything/modeling/image_encoder.py | 166,167 | 119,185 | forward | Block | 167,185 | 167,185 | class Block(nn.Module):
"""Transformer blocks with support of window attention and residual propagation blocks"""
def __init__(
self,
dim: int,
num_heads: int,
mlp_ratio: float = 4.0,
qkv_bias: bool = True,
norm_layer: Type[nn.Module] = nn.LayerNorm,
act_... | class Block(nn.Module):
"""Transformer blocks with support of window attention and residual propagation blocks"""
def __init__(
self,
dim: int,
num_heads: int,
mlp_ratio: float = 4.0,
qkv_bias: bool = True,
norm_layer: Type[nn.Module] = nn.LayerNorm,
act_... | shortcut = x
x = self.norm1(x)
# Window partition
if self.window_size > 0:
H, W = x.shape[1], x.shape[2]
x, pad_hw = window_partition(x, self.window_size)
x = self.attn(x)
# Reverse window partition
if self.window_size > 0:
x =... | shortcut = x
x = self.norm1(x)
# Window partition
if self.window_size > 0:
H, W = x.shape[1], x.shape[2]
x, pad_hw = window_partition(x, self.window_size)
x = self.attn(x)
# Reverse window partition
if self.window_size > 0:
x =... | 53 | 615 | 178 | 793 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... |
50,605 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/segment_anything/modeling/image_encoder.py | 106,107 | 17,119 | forward | ImageEncoderViT | 107,119 | 107,119 | class ImageEncoderViT(nn.Module):
def __init__(
self,
img_size: int = 1024,
patch_size: int = 16,
in_chans: int = 3,
embed_dim: int = 768,
depth: int = 12,
num_heads: int = 12,
mlp_ratio: float = 4.0,
out_chans: int = 256,
qkv_bias: boo... | class ImageEncoderViT(nn.Module):
def __init__(
self,
img_size: int = 1024,
patch_size: int = 16,
in_chans: int = 3,
embed_dim: int = 768,
depth: int = 12,
num_heads: int = 12,
mlp_ratio: float = 4.0,
out_chans: int = 256,
qkv_bias: boo... | x = self.patch_embed(x)
if self.pos_embed is not None:
x = x + self.pos_embed
for blk in self.blocks:
x = blk(x)
x = self.neck(x.permute(0, 3, 1, 2))
return x
| x = self.patch_embed(x)
if self.pos_embed is not None:
x = x + self.pos_embed
for blk in self.blocks:
x = blk(x)
x = self.neck(x.permute(0, 3, 1, 2))
return x
| 28 | 1,041 | 101 | 1,142 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... |
50,606 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/segment_anything/modeling/image_encoder.py | 391,392 | 364,396 | forward | PatchEmbed | 392,396 | 392,396 | class PatchEmbed(nn.Module):
"""
Image to Patch Embedding.
"""
def __init__(
self,
kernel_size: Tuple[int, int] = (16, 16),
stride: Tuple[int, int] = (16, 16),
padding: Tuple[int, int] = (0, 0),
in_chans: int = 3,
embed_dim: int = 768,
) -> None:
... | class PatchEmbed(nn.Module):
"""
Image to Patch Embedding.
"""
def __init__(
self,
kernel_size: Tuple[int, int] = (16, 16),
stride: Tuple[int, int] = (16, 16),
padding: Tuple[int, int] = (0, 0),
in_chans: int = 3,
embed_dim: int = 768,
) -> None:
... | x = self.proj(x)
# B C H W -> B H W C
x = x.permute(0, 2, 3, 1)
return x
| x = self.proj(x)
# B C H W -> B H W C
x = x.permute(0, 2, 3, 1)
return x
| 21 | 301 | 52 | 353 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... |
50,607 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/segment_anything/modeling/mask_decoder.py | 171,172 | 154,177 | forward | MLP | 172,177 | 172,177 | class MLP(nn.Module):
def __init__(
self,
input_dim: int,
hidden_dim: int,
output_dim: int,
num_layers: int,
sigmoid_output: bool = False,
) -> None:
super().__init__()
self.num_layers = num_layers
h = [hidden_dim] * (num_layers - 1)
... | class MLP(nn.Module):
def __init__(
self,
input_dim: int,
hidden_dim: int,
output_dim: int,
num_layers: int,
sigmoid_output: bool = False,
) -> None:
super().__init__()
self.num_layers = num_layers
h = [hidden_dim] * (num_layers - 1)
... | for i, layer in enumerate(self.layers):
x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
if self.sigmoid_output:
x = F.sigmoid(x)
return x
| for i, layer in enumerate(self.layers):
x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
if self.sigmoid_output:
x = F.sigmoid(x)
return x
| 23 | 189 | 75 | 264 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Type
from .common... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Type
from .common... |
50,608 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/segment_anything/modeling/mask_decoder.py | 71,79 | 16,154 | forward | MaskDecoder | 94,112 | 79,112 | class MaskDecoder(nn.Module):
def __init__(
self,
*,
transformer_dim: int,
transformer: nn.Module,
num_multimask_outputs: int = 3,
activation: Type[nn.Module] = nn.GELU,
iou_head_depth: int = 3,
iou_head_hidden_dim: int = 256,
) -> None:
""... | class MaskDecoder(nn.Module):
def __init__(
self,
*,
transformer_dim: int,
transformer: nn.Module,
num_multimask_outputs: int = 3,
activation: Type[nn.Module] = nn.GELU,
iou_head_depth: int = 3,
iou_head_hidden_dim: int = 256,
) -> None:
""... | masks, iou_pred = self.predict_masks(
image_embeddings=image_embeddings,
image_pe=image_pe,
sparse_prompt_embeddings=sparse_prompt_embeddings,
dense_prompt_embeddings=dense_prompt_embeddings,
)
# Select the correct mask or masks for output
... | """
Predict masks given image and prompt embeddings.
Arguments:
image_embeddings (torch.Tensor): the embeddings from the image encoder
image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
sparse_prompt_embeddings (torch.Tensor): the embed... | 45 | 1,601 | 201 | 1,802 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Type
from .common... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Type
from .common... |
50,609 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/segment_anything/modeling/prompt_encoder.py | 219,222 | 182,227 | forward_with_coords | PositionEmbeddingRandom | 223,227 | 222,227 | class PositionEmbeddingRandom(nn.Module):
"""
Positional encoding using random spatial frequencies.
"""
def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
super().__init__()
if scale is None or scale <= 0.0:
scale = 1.0
self.register_... | class PositionEmbeddingRandom(nn.Module):
"""
Positional encoding using random spatial frequencies.
"""
def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
super().__init__()
if scale is None or scale <= 0.0:
scale = 1.0
self.register_... | coords = coords_input.clone()
coords[:, :, 0] = coords[:, :, 0] / image_size[1]
coords[:, :, 1] = coords[:, :, 1] / image_size[0]
return self._pe_encoding(coords.to(torch.float)) # B x N x C
| """Positionally encode points that are not normalized to [0,1]."""
coords = coords_input.clone()
coords[:, :, 0] = coords[:, :, 0] / image_size[1]
coords[:, :, 1] = coords[:, :, 1] / image_size[0]
return self._pe_encoding(coords.to(torch.float)) # B x N x C
| 29 | 612 | 98 | 710 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from torch import nn
from typing import Any, Optional, Tuple, Type
from .common import L... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from torch import nn
from typing import Any, Optional, Tuple, Type
from .common import L... |
50,611 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/segment_anything/modeling/transformer.py | 213,214 | 185,241 | _recombine_heads | Attention | 214,218 | 214,218 | class Attention(nn.Module):
"""
An attention layer that allows for downscaling the size of the embedding
after projection to queries, keys, and values.
"""
def __init__(
self,
embedding_dim: int,
num_heads: int,
downsample_rate: int = 1,
) -> None:
super(... | class Attention(nn.Module):
"""
An attention layer that allows for downscaling the size of the embedding
after projection to queries, keys, and values.
"""
def __init__(
self,
embedding_dim: int,
num_heads: int,
downsample_rate: int = 1,
) -> None:
super(... | b, n_heads, n_tokens, c_per_head = x.shape
x = x.transpose(1, 2)
return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C
| b, n_heads, n_tokens, c_per_head = x.shape
x = x.transpose(1, 2)
return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C
| 22 | 679 | 78 | 757 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import Tensor, nn
import math
from typing import Tuple, Type
from .common import MLPBlock
clas... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import Tensor, nn
import math
from typing import Tuple, Type
from .common import MLPBlock
clas... |
50,612 | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything | /home/hieuvd/dung_repo/raw/stars_03/yatengLG_ISAT_with_segment_anything/ISAT_with_segment_anything/ISAT/segment_any/segment_anything/modeling/transformer.py | 151,154 | 109,185 | forward | TwoWayAttentionBlock | 154,185 | 154,185 | class TwoWayAttentionBlock(nn.Module):
def __init__(
self,
embedding_dim: int,
num_heads: int,
mlp_dim: int = 2048,
activation: Type[nn.Module] = nn.ReLU,
attention_downsample_rate: int = 2,
skip_first_layer_pe: bool = False,
) -> None:
"""
... | class TwoWayAttentionBlock(nn.Module):
def __init__(
self,
embedding_dim: int,
num_heads: int,
mlp_dim: int = 2048,
activation: Type[nn.Module] = nn.ReLU,
attention_downsample_rate: int = 2,
skip_first_layer_pe: bool = False,
) -> None:
"""
... | # Self attention block
if self.skip_first_layer_pe:
queries = self.self_attn(q=queries, k=queries, v=queries)
else:
q = queries + query_pe
attn_out = self.self_attn(q=q, k=q, v=queries)
queries = queries + attn_out
queries = self.norm1(quer... | # Self attention block
if self.skip_first_layer_pe:
queries = self.self_attn(q=queries, k=queries, v=queries)
else:
q = queries + query_pe
attn_out = self.self_attn(q=q, k=q, v=queries)
queries = queries + attn_out
queries = self.norm1(quer... | 111 | 552 | 361 | 913 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import Tensor, nn
import math
from typing import Tuple, Type
from .common import MLPBlock
clas... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import Tensor, nn
import math
from typing import Tuple, Type
from .common import MLPBlock
clas... |
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