5595037279

pending completion

Build # 5595037279

Build Type

push

github

Committed by

yeliudev

Commit Message

Add contrastive losses support

Run Details

61 of 61 new or added lines in 4 files covered. (100.0%)

648 of 4026 relevant lines covered (16.1%)

3.13 hits per line

Source File
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0.0

/nncore/nn/bundle/bundle.py

# Copyright (c) Ye Liu. Licensed under the MIT License.

from collections import OrderedDict

import torch
import torch.nn as nn

import nncore


class Sequential(nn.Sequential):
    """
    An :obj:`nn.Sequential` class that supports multiple inputs.
    """

    def __init__(self, *args):
        super(nn.Sequential, self).__init__()

        idx = 0
        for arg in args:
            if isinstance(arg, OrderedDict):
                for key, mod in arg.items():
                    if mod is not None:
                        self.add_module(key, mod)
                continue
            elif not isinstance(arg, (list, tuple)):
                arg = [arg]

            for mod in [a for a in arg if a is not None]:
                self.add_module(str(idx), mod)
                idx += 1

    def forward(self, *args, **kwargs):
        for mod in self:
            if isinstance(args, (list, tuple)):
                args = mod(*args, **kwargs)
            else:
                args = mod(args, **kwargs)
        return args


class ModuleList(nn.ModuleList):
    """
    An :obj:`nn.ModuleList` class that supports multiple inputs.
    """

    def __init__(self, *args):
        super(nn.ModuleList, self).__init__()
        self += [a for a in nncore.flatten(args) if a is not None]


class ModuleDict(nn.ModuleDict):
    """
    An :obj:`nn.ModuleDict` class that supports multiple inputs.
    """

    def __init__(self, *args, **kwargs):
        super(ModuleDict, self).__init__()
        for arg in args:
            self.update(arg)
        self.update(kwargs)


class Parameter(nn.Parameter):
    """
    An :obj:`nn.Parameter` class that supports multiple inputs initializes the
    parameters using a scaled normal distribution.
    """

    def __new__(cls, *args, requires_grad=True, **kwargs):
        if torch.is_tensor(args[0]):
            data = args[0]
        elif isinstance(args[0], float):
            data = torch.Tensor([args[0]])
        elif isinstance(args[0], (list, tuple)):
            data = torch.randn(args[0], **kwargs) / args[0][-1]**0.5
        else:
            data = torch.randn(args, **kwargs) / args[-1]**0.5

        return torch.Tensor._make_subclass(cls, data, requires_grad)

1	# Copyright (c) Ye Liu. Licensed under the MIT License.
2
3	from collections import OrderedDict	×
4
5	import torch	×
6	import torch.nn as nn	×
7
8	import nncore	×
9
10
11	class Sequential(nn.Sequential):	×
12	"""
13	An :obj:`nn.Sequential` class that supports multiple inputs.
14	"""
15
16	def __init__(self, *args):	×
17	super(nn.Sequential, self).__init__()	×
18
19	idx = 0	×
20	for arg in args:	×
21	if isinstance(arg, OrderedDict):	×
22	for key, mod in arg.items():	×
23	if mod is not None:	×
24	self.add_module(key, mod)	×
25	continue	×
26	elif not isinstance(arg, (list, tuple)):	×
27	arg = [arg]	×
28
29	for mod in [a for a in arg if a is not None]:	×
30	self.add_module(str(idx), mod)	×
31	idx += 1	×
32
33	def forward(self, args, *kwargs):	×
34	for mod in self:	×
35	if isinstance(args, (list, tuple)):	×
36	args = mod(args, *kwargs)	×
37	else:
38	args = mod(args, **kwargs)	×
39	return args	×
40
41
42	class ModuleList(nn.ModuleList):	×
43	"""
44	An :obj:`nn.ModuleList` class that supports multiple inputs.
45	"""
46
47	def __init__(self, *args):	×
48	super(nn.ModuleList, self).__init__()	×
49	self += [a for a in nncore.flatten(args) if a is not None]	×
50
51
52	class ModuleDict(nn.ModuleDict):	×
53	"""
54	An :obj:`nn.ModuleDict` class that supports multiple inputs.
55	"""
56
57	def __init__(self, args, *kwargs):	×
58	super(ModuleDict, self).__init__()	×
59	for arg in args:	×
60	self.update(arg)	×
61	self.update(kwargs)	×
62
63
64	class Parameter(nn.Parameter):	×
65	"""
66	An :obj:`nn.Parameter` class that supports multiple inputs initializes the
67	parameters using a scaled normal distribution.
68	"""
69
70	def __new__(cls, args, requires_grad=True, *kwargs):	×
71	if torch.is_tensor(args[0]):	×
72	data = args[0]	×
73	elif isinstance(args[0], float):	×
74	data = torch.Tensor([args[0]])	×
75	elif isinstance(args[0], (list, tuple)):	×
76	data = torch.randn(args[0], kwargs) / args[0][-1]0.5	×
77	else:
78	data = torch.randn(args, kwargs) / args[-1]0.5	×
79
80	return torch.Tensor._make_subclass(cls, data, requires_grad)	×

yeliudev / nncore / 5595037279

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