Method__init__(self, opts, scale=0.875, random_crop=False,
random_hflip=False, random_vflip=False,
pretrainedmodels/utils.py:36
Method__init__(self,in_channels,out_channels,kernel_size=1,stride=1,padding=0,dilation=1,bias=False)
pretrainedmodels/models/xception.py:51
Method__init__(self, in_planes, out_planes, kernel_size, stride, padding=0)
pretrainedmodels/models/inceptionv4.py:37
Method__init__(self, in_planes, out_planes, kernel_size, stride, padding=0)
pretrainedmodels/models/inceptionresnetv2.py:36
Method__init__(self, in_channels, out_channels, dw_kernel_size, dw_stride,
dw_padding)
pretrainedmodels/models/pnasnet.py:51
Method__init__(self, in_channels, out_channels, kernel_size, stride=1,
stem_cell=False, zero_pad=False)
pretrainedmodels/models/pnasnet.py:69
Method__init__(self, inplanes, planes, groups, reduction, stride=1,
downsample=None)
pretrainedmodels/models/senet.py:140
Method__init__(self, inplanes, planes, groups, reduction, stride=1,
downsample=None)
pretrainedmodels/models/senet.py:166
Method__init__(self, inplanes, planes, groups, reduction, stride=1,
downsample=None, base_width=4)
pretrainedmodels/models/senet.py:189
Method__init__(self, local_size=1, alpha=1.0, beta=0.75, k=1, ACROSS_CHANNELS=True)
pretrainedmodels/models/vggm.py:25
Method__init__(self, in_chs, out_chs, kernel_size, stride,
padding=0, groups=1, activation_fn=nn.ReLU(inpla
pretrainedmodels/models/dpn.py:219
Method__init__(
self, in_chs, num_1x1_a, num_3x3_b, num_1x1_c, inc, groups, block_type='normal', b=False)
pretrainedmodels/models/dpn.py:249
Method__init__(self, in_channels, out_channels, dw_kernel, dw_stride, dw_padding, bias=False)
pretrainedmodels/models/nasnet.py:62
Method__init__(self, in_channels, out_channels, kernel_size, stride, padding, bias=False)
pretrainedmodels/models/nasnet.py:79
Method__init__(self, in_channels, out_channels, kernel_size, stride, padding, bias=False)
pretrainedmodels/models/nasnet.py:100
Method__init__(self, in_channels, out_channels, kernel_size, stride, padding, z_padding=1, bias=False)
pretrainedmodels/models/nasnet.py:121
Method__init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0,
output_relu=True)
pretrainedmodels/models/polynet.py:25
Method__init__(self, in_planes, out_planes, kernel_size, num_blocks,
stride=1, padding=0)
pretrainedmodels/models/polynet.py:49
Method__init__(self, in_channels, out_channels, dw_kernel, dw_stride, dw_padding, bias=False)
pretrainedmodels/models/nasnet_mobile.py:78
Method__init__(self, in_channels, out_channels, kernel_size, stride, padding, name=None, bias=False)
pretrainedmodels/models/nasnet_mobile.py:95
Method__init__(self, in_channels, out_channels, kernel_size, stride, padding, bias=False)
pretrainedmodels/models/nasnet_mobile.py:122
Method__init__(self, in_channels, out_channels, kernel_size, stride, padding, z_padding=1, bias=False)
pretrainedmodels/models/nasnet_mobile.py:143