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class BilinearScale(mx.operator.CustomOp):
def __init__(self, scale):
self.scale = scale
def forward(self, is_train, req, in_data, out_data, aux):
x = in_data[0]
(h, w) = x.shape[2:]
new_h = (int(((h - 1) * self.scale)) + 1)
new_w = (int(((w - 1) * self.scale)) + 1)
... |
@mx.operator.register('BilinearScale')
class BilinearScaleProp(mx.operator.CustomOpProp):
def __init__(self, scale):
super(BilinearScaleProp, self).__init__(need_top_grad=True)
self.scale = float(scale)
def infer_shape(self, in_shape):
(n, c, h, w) = in_shape[0]
new_h = (int(... |
class BilinearScaleLike(mx.operator.CustomOp):
def forward(self, is_train, req, in_data, out_data, aux):
(x, x_ref) = in_data
(new_h, new_w) = x_ref.shape[2:]
x.attach_grad()
with mx.autograd.record():
new_x = mx.nd.contrib.BilinearResize2D(x, height=new_h, width=new_w... |
@mx.operator.register('BilinearScaleLike')
class BilinearScaleLikeProp(mx.operator.CustomOpProp):
def __init__(self):
super(BilinearScaleLikeProp, self).__init__(need_top_grad=True)
def list_arguments(self):
return ['d1', 'd2']
def infer_shape(self, in_shape):
out_shape = list(i... |
class SegmentLoss(mx.operator.CustomOp):
def __init__(self, has_grad_scale):
self.has_grad_scale = has_grad_scale
def forward(self, is_train, req, in_data, out_data, aux):
prediction = mx.nd.softmax(in_data[0], axis=1)
self.assign(out_data[0], req[0], prediction)
def backward(se... |
@mx.operator.register('SegmentLoss')
class SegmentLossProp(mx.operator.CustomOpProp):
def __init__(self, has_grad_scale=0):
super(SegmentLossProp, self).__init__(need_top_grad=False)
self.has_grad_scale = (int(has_grad_scale) > 0)
def list_arguments(self):
if self.has_grad_scale:
... |
class CompletionLoss(mx.operator.CustomOp):
def __init__(self, has_grad_scale):
self.has_grad_scale = has_grad_scale
def forward(self, is_train, req, in_data, out_data, aux):
prediction = mx.nd.softmax(in_data[0], axis=1)
self.assign(out_data[0], req[0], prediction)
def backward... |
@mx.operator.register('CompletionLoss')
class CompletionLossProp(mx.operator.CustomOpProp):
def __init__(self, has_grad_scale=0):
super(CompletionLossProp, self).__init__(need_top_grad=False)
self.has_grad_scale = (int(has_grad_scale) > 0)
def list_arguments(self):
if self.has_grad_s... |
class MultiSigmoidLoss(mx.operator.CustomOp):
def forward(self, is_train, req, in_data, out_data, aux):
(logit, label) = in_data
prediction = mx.nd.sigmoid(logit, axis=1)
self.assign(out_data[0], req[0], prediction)
def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
... |
@mx.operator.register('MultiSigmoidLoss')
class MultiSigmoidLossProp(mx.operator.CustomOpProp):
def __init__(self):
super(MultiSigmoidLossProp, self).__init__(need_top_grad=False)
def list_arguments(self):
return ['data', 'label']
def list_outputs(self):
return ['output']
d... |
def config_efficientnet(model_name):
assert re.match('^efficientnet-b[0-7]$', model_name), model_name
efficientnet_params = DEFAULT_EFFICIENT_PARAMS[model_name]
block_args = DEFAULT_EFFICIENT_BLOCK_ARGS
(width_coefficient, depth_coefficient, resolution, dropout_rate) = efficientnet_params
global_p... |
def MBConvBlock(data, block_args, global_params, use_global_stats, block_id, name, lr_mult, reuse, input_size=None):
if block_args.super_pixel:
raise NotImplementedError
if block_args.condconv:
raise NotImplementedError
kernel = ((block_args.kernel_size,) * 2)
dilate = (((1 if (kernel[... |
def MBConvBlockWithoutDepthwise(data, block_args, global_params, use_global_stats, begin_id, name, lr_mult, reuse):
raise NotImplementedError
|
def meta_efficientnet(model_name, get_internals=False, input_size=None):
(block_args_list, global_params) = config_efficientnet(model_name)
def round_filters(num_filters):
multiplier = global_params.width_coefficient
if (not multiplier):
return num_filters
divisor = global... |
def tf2mx_params(ckpt_file, dst_file=None, name='', use_ema=True):
convert_w = (lambda x: mx.nd.array((x.transpose(3, 2, 0, 1) if (x.ndim == 4) else x.T)))
convert_b = (lambda x: mx.nd.array(x))
convert_dp_w = (lambda x: mx.nd.array(x.transpose(2, 3, 0, 1)))
lookup_ptype = {'kernel': ('arg', 'weight',... |
def incepConv(data, num_filter, kernel, stride=None, dilate=None, pad=None, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None):
assert (name is not None)
x = Conv(data, num_filter, kernel, stride, dilate, pad, name=('conv_%s' % name), lr_mult=lr_mult, reuse=reuse)
x = BN(x,... |
def incepBlockA(data, num_filter_1, num_filter_3r, num_filter_3, num_filter_d3r, num_filter_d3, num_filter_p, pool_type, dilate=1, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None):
assert (name is not None)
x1 = incepConv(data, num_filter_1, (1, 1), momentum=momentum, eps=eps... |
def incepBlockB(data, num_filter_3r, num_filter_3, num_filter_d3r, num_filter_d3, stride=2, dilate=1, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None):
assert (name is not None)
x3 = incepConv(data, num_filter_3r, (1, 1), momentum=momentum, eps=eps, use_global_stats=use_globa... |
def inceptionBN(x, momentum=0.9, eps=1e-05, use_global_stats=False, bn_data=True, name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
if bn_data:
x = BN(x, fix_gamma=True, momentum=momentum, eps=eps, use_global_stats=use_global_stats, name=(name + 'bn_data'), reuse=reuse)
x ... |
class OpConstant(mx.operator.CustomOp):
def __init__(self, val):
self.val = val
def forward(self, is_train, req, in_data, out_data, aux):
self.assign(out_data[0], req[0], self.val)
def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
pass
|
@mx.operator.register('Constant')
class OpConstantProp(mx.operator.CustomOpProp):
def __init__(self, val_str, shape_str, type_str='float32'):
super(OpConstantProp, self).__init__(need_top_grad=False)
val = [float(x) for x in val_str.split(',')]
shape = [int(x) for x in shape_str.split(','... |
def CustomConstantEncoder(value, dtype='float32'):
if (not isinstance(value, np.ndarray)):
if (not isinstance(value, (list, tuple))):
value = [value]
value = np.array(value, dtype=dtype)
return (','.join([str(x) for x in value.ravel()]), ','.join([str(x) for x in value.shape]))
|
def Constant(value, dtype='float32'):
assert isinstance(dtype, str), dtype
(val, shape) = CustomConstantEncoder(value, dtype)
return mx.sym.Custom(val_str=val, shape_str=shape, type_str=dtype, op_type='Constant')
|
class DropConnect(mx.operator.CustomOp):
def __init__(self, p):
self.drop_rate = p
self.mask = None
def forward(self, is_train, req, in_data, out_data, aux):
data = in_data[0]
if (is_train or (self.drop_rate == 0)):
mask_shape = ([data.shape[0]] + ([1] * (len(data... |
@mx.operator.register('DropConnect')
class DropConnectProp(mx.operator.CustomOpProp):
def __init__(self, p):
super(DropConnectProp, self).__init__(need_top_grad=True)
self.drop_rate = float(p)
assert ((self.drop_rate >= 0) and (self.drop_rate < 1))
def list_arguments(self):
r... |
class BilinearScale(mx.operator.CustomOp):
def __init__(self, scale):
self.scale = scale
def forward(self, is_train, req, in_data, out_data, aux):
x = in_data[0]
(h, w) = x.shape[2:]
new_h = (int(((h - 1) * self.scale)) + 1)
new_w = (int(((w - 1) * self.scale)) + 1)
... |
@mx.operator.register('BilinearScale')
class BilinearScaleProp(mx.operator.CustomOpProp):
def __init__(self, scale):
super(BilinearScaleProp, self).__init__(need_top_grad=True)
self.scale = float(scale)
def infer_shape(self, in_shape):
(n, c, h, w) = in_shape[0]
new_h = (int(... |
class BilinearScaleLike(mx.operator.CustomOp):
def forward(self, is_train, req, in_data, out_data, aux):
(x, x_ref) = in_data
(new_h, new_w) = x_ref.shape[2:]
x.attach_grad()
with mx.autograd.record():
new_x = mx.nd.contrib.BilinearResize2D(x, height=new_h, width=new_w... |
@mx.operator.register('BilinearScaleLike')
class BilinearScaleLikeProp(mx.operator.CustomOpProp):
def __init__(self):
super(BilinearScaleLikeProp, self).__init__(need_top_grad=True)
def list_arguments(self):
return ['d1', 'd2']
def infer_shape(self, in_shape):
out_shape = list(i... |
class SegmentLoss(mx.operator.CustomOp):
def __init__(self, has_grad_scale):
self.has_grad_scale = has_grad_scale
def forward(self, is_train, req, in_data, out_data, aux):
prediction = mx.nd.softmax(in_data[0], axis=1)
self.assign(out_data[0], req[0], prediction)
def backward(se... |
@mx.operator.register('SegmentLoss')
class SegmentLossProp(mx.operator.CustomOpProp):
def __init__(self, has_grad_scale=0):
super(SegmentLossProp, self).__init__(need_top_grad=False)
self.has_grad_scale = (int(has_grad_scale) > 0)
def list_arguments(self):
if self.has_grad_scale:
... |
class MultiSigmoidLoss(mx.operator.CustomOp):
def forward(self, is_train, req, in_data, out_data, aux):
(logit, label) = in_data
prediction = mx.nd.sigmoid(logit, axis=1)
self.assign(out_data[0], req[0], prediction)
def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
... |
@mx.operator.register('MultiSigmoidLoss')
class MultiSigmoidLossProp(mx.operator.CustomOpProp):
def __init__(self):
super(MultiSigmoidLossProp, self).__init__(need_top_grad=False)
def list_arguments(self):
return ['data', 'label']
def list_outputs(self):
return ['output']
d... |
class MultiSoftmaxLoss(mx.operator.CustomOp):
def forward(self, is_train, req, in_data, out_data, aux):
(logit, label) = in_data
prediction = mx.nd.softmax(logit, axis=1)
self.assign(out_data[0], req[0], prediction)
def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
... |
@mx.operator.register('MultiSoftmaxLoss')
class MultiSoftmaxLossProp(mx.operator.CustomOpProp):
def __init__(self):
super(MultiSoftmaxLossProp, self).__init__(need_top_grad=False)
def list_arguments(self):
return ['data', 'label']
def list_outputs(self):
return ['output']
d... |
def ResStem(data, num_filter, momentum=0.9, eps=1e-05, use_global_stats=False, bn_data=True, name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
if bn_data:
x = BN(data, fix_gamma=True, momentum=momentum, eps=eps, use_global_stats=use_global_stats, name=(name + 'bn_data'), reuse... |
def ResUnit(data, num_filter, stride, dilate, projection, bottle_neck, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None):
assert (name is not None)
x = BNRelu(data, fix_gamma=False, momentum=momentum, eps=eps, use_global_stats=use_global_stats, name=(name + '_bn1'), lr_mult=lr... |
def ResBlock(data, num_unit, num_filter, stride, dilate, bottle_neck, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None):
assert (name is not None)
x = ResUnit(data, num_filter, stride, dilate, True, bottle_neck, momentum, eps, use_global_stats, (name + '_unit1'), lr_mult, reus... |
def _Resnet(x, num_units, num_filters, bottle_neck, momentum=0.9, eps=1e-05, use_global_stats=False, bn_data=True, strides=(1, 2, 2, 2), dilates=(1, 1, 1, 1), name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
x = ResStem(x, num_filters[0], momentum, eps, use_global_stats, bn_data, nam... |
def resnet18(x, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
x = _Resnet(x, (2, 2, 2, 2), (64, 64, 128, 256, 512), False, momentum, eps, use_global_stats, name=name, lr_mult=lr_mult, reuse=reuse)
x = Pool(x, (1, 1), pool_type='... |
def resnet34(x, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
x = _Resnet(x, (3, 4, 6, 3), (64, 64, 128, 256, 512), False, momentum, eps, use_global_stats, name=name, lr_mult=lr_mult, reuse=reuse)
x = Pool(x, (1, 1), pool_type='... |
def resnet50(x, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
x = _Resnet(x, (3, 4, 6, 3), (64, 256, 512, 1024, 2048), True, momentum, eps, use_global_stats, name=name, lr_mult=lr_mult, reuse=reuse)
x = Pool(x, (1, 1), pool_type... |
def resnet101(x, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
x = _Resnet(x, (3, 4, 23, 3), (64, 256, 512, 1024, 2048), True, momentum, eps, use_global_stats, name=name, lr_mult=lr_mult, reuse=reuse)
x = Pool(x, (1, 1), pool_ty... |
def resnet101_largefov(x, num_cls, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=10, reuse=None):
name = ('' if (name is None) else name)
x = _Resnet(x, (3, 4, 23, 3), (64, 256, 512, 1024, 2048), True, momentum, eps, use_global_stats, strides=(1, 2, 1, 1), dilates=(1, 1, 2, 4), name=name... |
def resnet101_aspp(x, num_cls, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=10, reuse=None):
name = ('' if (name is None) else name)
x = _Resnet(x, (3, 4, 23, 3), (64, 256, 512, 1024, 2048), True, momentum, eps, use_global_stats, strides=(1, 2, 1, 1), dilates=(1, 1, 2, 4), name=name, lr... |
def ResStemV1(data, num_filter, momentum=0.9, eps=1e-05, use_global_stats=False, bn_data=True, name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
if bn_data:
x = BN(data, fix_gamma=True, momentum=momentum, eps=eps, use_global_stats=use_global_stats, name=(name + 'bn_data'), reu... |
def ResUnitV1(data, num_filter, stride, dilate, projection, bottle_neck, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None):
assert (name is not None)
if projection:
shortcut = Conv(data, num_filter=num_filter, kernel=(1, 1), stride=((stride,) * 2), pad=(0, 0), no_bias=... |
def ResBlockV1(data, num_unit, num_filter, stride, dilate, bottle_neck, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None):
assert (name is not None)
x = ResUnitV1(data, num_filter, stride, dilate, True, bottle_neck, momentum, eps, use_global_stats, (name + '_unit1'), lr_mult, ... |
def _Resnet(x, num_units, num_filters, bottle_neck, momentum=0.9, eps=1e-05, use_global_stats=False, bn_data=True, strides=(1, 2, 2, 2), dilates=(1, 1, 1, 1), name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
x = ResStemV1(x, num_filters[0], momentum, eps, use_global_stats, bn_data, n... |
def resnet18(x, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
x = _Resnet(x, (2, 2, 2, 2), (64, 64, 128, 256, 512), False, momentum, eps, use_global_stats, name=name, lr_mult=lr_mult, reuse=reuse)
x = Pool(x, (1, 1), pool_type='... |
def resnet34(x, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
x = _Resnet(x, (3, 4, 6, 3), (64, 64, 128, 256, 512), False, momentum, eps, use_global_stats, name=name, lr_mult=lr_mult, reuse=reuse)
x = Pool(x, (1, 1), pool_type='... |
def resnet50(x, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
x = _Resnet(x, (3, 4, 6, 3), (64, 256, 512, 1024, 2048), True, momentum, eps, use_global_stats, name=name, lr_mult=lr_mult, reuse=reuse)
x = Pool(x, (1, 1), pool_type... |
def resnet101(x, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
x = _Resnet(x, (3, 4, 23, 3), (64, 256, 512, 1024, 2048), True, momentum, eps, use_global_stats, name=name, lr_mult=lr_mult, reuse=reuse)
x = Pool(x, (1, 1), pool_ty... |
def resnet101_largefov(x, num_cls, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=10, reuse=None):
name = ('' if (name is None) else name)
x = _Resnet(x, (3, 4, 23, 3), (64, 256, 512, 1024, 2048), True, momentum, eps, use_global_stats, strides=(1, 2, 1, 1), dilates=(1, 1, 2, 4), name=name... |
def resnet101_aspp(x, num_cls, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=10, reuse=None):
name = ('' if (name is None) else name)
x = _Resnet(x, (3, 4, 23, 3), (64, 256, 512, 1024, 2048), True, momentum, eps, use_global_stats, strides=(1, 2, 1, 1), dilates=(1, 1, 2, 4), name=name, lr... |
def vgg16(x, name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
x = ConvRelu(x, 64, (3, 3), pad=(1, 1), name=(name + 'conv1_1'), lr_mult=lr_mult, reuse=reuse)
x = ConvRelu(x, 64, (3, 3), pad=(1, 1), name=(name + 'conv1_2'), lr_mult=lr_mult, reuse=reuse)
x = Pool(x, (2, 2), name... |
def vgg16_deeplab(x, name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
x = ConvRelu(x, 64, (3, 3), pad=(1, 1), name=(name + 'conv1_1'), lr_mult=lr_mult, reuse=reuse)
x = ConvRelu(x, 64, (3, 3), pad=(1, 1), name=(name + 'conv1_2'), lr_mult=lr_mult, reuse=reuse)
x = Pool(x, kern... |
def vgg16_largefov(x, num_cls, name=None, lr_mult=10, reuse=None):
name = ('' if (name is None) else name)
x = vgg16_deeplab(x, name, lr_mult=1, reuse=reuse)
x = ConvRelu(x, 1024, (3, 3), dilate=(12, 12), pad=(12, 12), name=(name + 'fc6'), reuse=reuse)
x = Drop(x, 0.5, name=(name + 'drop6'))
x = C... |
def vgg16_aspp(x, num_cls, name=None, lr_mult=10, reuse=None):
name = ('' if (name is None) else name)
x_backbone = vgg16_deeplab(x, name, lr_mult=1, reuse=reuse)
x_aspp = []
for d in (6, 12, 18, 24):
x = ConvRelu(x_backbone, 1024, (3, 3), dilate=(d, d), pad=(d, d), name=(name + ('fc6_aspp%d' ... |
def wResStem(data, num_filter, momentum=0.9, eps=1e-05, use_global_stats=False, bn_data=True, name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
if bn_data:
x = BN(data, fix_gamma=True, momentum=momentum, eps=eps, use_global_stats=use_global_stats, name=(name + 'bn_data'), reus... |
def wResUnit(data, num_filter, stride, dilate, projection, bottle_neck, dropout=0, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None, **kwargs):
assert (name is not None)
x = BNRelu(data, fix_gamma=False, momentum=momentum, eps=eps, use_global_stats=use_global_stats, name=(('bn... |
def wResBlock(data, num_unit, num_filter, stride, dilate, bottle_neck, dropout=0, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None, **kwargs):
assert (name is not None)
x = wResUnit(data, num_filter, stride, dilate, True, bottle_neck, dropout, momentum, eps, use_global_stats, ... |
def wresnet38(x, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, out_internals=False, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
internals = []
x = wResStem(x, 64, momentum, eps, use_global_stats, bn_data=True, name=name, lr_mult=lr_mult, reuse=reuse)
x = wResBlock(x, ... |
def MultiScale(scales):
scales = [s for s in scales if (s != 1)]
def func_wrapper(model_func):
def model_func_ms(*args, **kwargs):
assert (len(args) > 0), 'Cannot find input variable'
input_var = args[0]
args = args[1:]
out_0 = model_func(*((input_var,... |
def ResStem(data, num_filter, momentum=0.9, eps=1e-05, use_global_stats=False, bn_data=True, name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
if bn_data:
x = BN(data, fix_gamma=True, momentum=momentum, eps=eps, use_global_stats=use_global_stats, name=(name + 'bn_data'), reuse... |
def ResUnit(data, num_filter, stride, dilate, projection, bottle_neck, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None):
assert (name is not None)
x = BNRelu(data, fix_gamma=False, momentum=momentum, eps=eps, use_global_stats=use_global_stats, name=(name + '_bn1'), lr_mult=lr... |
def ResBlock(data, num_unit, num_filter, stride, dilate, bottle_neck, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None):
assert (name is not None)
x = ResUnit(data, num_filter, stride, dilate, True, bottle_neck, momentum, eps, use_global_stats, (name + '_unit1'), lr_mult, reus... |
def _Resnet(x, num_units, num_filters, bottle_neck, momentum=0.9, eps=1e-05, use_global_stats=False, bn_data=True, strides=(1, 2, 2, 2), dilates=(1, 1, 1, 1), name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
x = ResStem(x, num_filters[0], momentum, eps, use_global_stats, bn_data, nam... |
def resnet18(x, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
x = _Resnet(x, (2, 2, 2, 2), (64, 64, 128, 256, 512), False, momentum, eps, use_global_stats, name=name, lr_mult=lr_mult, reuse=reuse)
x = Pool(x, (1, 1), pool_type='... |
def resnet34(x, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
x = _Resnet(x, (3, 4, 6, 3), (64, 64, 128, 256, 512), False, momentum, eps, use_global_stats, name=name, lr_mult=lr_mult, reuse=reuse)
x = Pool(x, (1, 1), pool_type='... |
def resnet50(x, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
x = _Resnet(x, (3, 4, 6, 3), (64, 256, 512, 1024, 2048), True, momentum, eps, use_global_stats, name=name, lr_mult=lr_mult, reuse=reuse)
x = Pool(x, (1, 1), pool_type... |
def resnet101(x, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=1, reuse=None):
name = ('' if (name is None) else name)
x = _Resnet(x, (3, 4, 23, 3), (64, 256, 512, 1024, 2048), True, momentum, eps, use_global_stats, name=name, lr_mult=lr_mult, reuse=reuse)
x = Pool(x, (1, 1), pool_ty... |
def resnet50_largefov(x, num_cls, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=10, reuse=None):
name = ('' if (name is None) else name)
x = _Resnet(x, (3, 4, 6, 3), (64, 256, 512, 1024, 2048), True, momentum, eps, use_global_stats, strides=(1, 2, 1, 1), dilates=(1, 1, 2, 4), name=name, ... |
def resnet101_largefov(x, num_cls, momentum=0.9, eps=1e-05, use_global_stats=False, name=None, lr_mult=10, reuse=None):
name = ('' if (name is None) else name)
x = _Resnet(x, (3, 4, 23, 3), (64, 256, 512, 1024, 2048), True, momentum, eps, use_global_stats, strides=(1, 2, 1, 1), dilates=(1, 1, 2, 4), name=name... |
class _VOC_proto(object):
@staticmethod
def _get_palette():
def bitget(bit, idx):
return ((bit & (1 << idx)) > 0)
cmap = []
for i in range(256):
(r, g, b) = (0, 0, 0)
idx = i
for j in range(8):
r = (r | (bitget(idx, 0) <... |
def imwrite(filename, image):
dirname = os.path.dirname(filename)
if (not os.path.exists(dirname)):
try:
os.makedirs(dirname)
except:
pass
cv2.imwrite(filename, image)
|
def npsave(filename, data):
dirname = os.path.dirname(filename)
if (not os.path.exists(dirname)):
try:
os.makedirs(dirname)
except:
pass
np.save(filename, data)
|
def pkldump(filename, data):
dirname = os.path.dirname(filename)
if (not os.path.exists(dirname)):
try:
os.makedirs(dirname)
except:
pass
with open(filename, 'wb') as f:
pickle.dump(data, f)
|
def imhstack(images, height=None):
images = as_list(images)
images = list(map(image2C3, images))
if (height is None):
height = np.array([img.shape[0] for img in images]).max()
images = [resize_height(img, height) for img in images]
if (len(images) == 1):
return images[0]
images... |
def imvstack(images, width=None):
images = as_list(images)
images = list(map(image2C3, images))
if (width is None):
width = np.array([img.shape[1] for img in images]).max()
images = [resize_width(img, width) for img in images]
if (len(images) == 1):
return images[0]
images = [[... |
def as_list(data):
if (not isinstance(data, (list, tuple))):
return [data]
return list(data)
|
def image2C3(image):
if (image.ndim == 3):
return image
if (image.ndim == 2):
return np.repeat(image[(..., np.newaxis)], 3, axis=2)
raise ValueError('image.ndim = {}, invalid image.'.format(image.ndim))
|
def resize_height(image, height):
if (image.shape[0] == height):
return image
(h, w) = image.shape[:2]
width = ((height * w) // h)
image = cv2.resize(image, (width, height))
return image
|
def resize_width(image, width):
if (image.shape[1] == width):
return image
(h, w) = image.shape[:2]
height = ((width * h) // w)
image = cv2.resize(image, (width, height))
return image
|
def imtext(image, text, space=(3, 3), color=(0, 0, 0), thickness=1, fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1.0):
assert isinstance(text, str), type(text)
size = cv2.getTextSize(text, fontFace, fontScale, thickness)
image = cv2.putText(image, text, (space[0], (size[1] + space[1])), fontFace, fontScal... |
def setGPU(gpus):
len_gpus = len(gpus.split(','))
os.environ['CUDA_VISIBLE_DEVICES'] = gpus
gpus = ','.join(map(str, range(len_gpus)))
return gpus
|
def getTime():
return datetime.now().strftime('%m-%d %H:%M:%S')
|
class Timer(object):
curr_record = None
prev_record = None
@classmethod
def record(cls):
cls.prev_record = cls.curr_record
cls.curr_record = time.time()
@classmethod
def interval(cls):
if (cls.prev_record is None):
return 0
return (cls.curr_record ... |
def wrapColor(string, color):
try:
header = {'red': '\x1b[91m', 'green': '\x1b[92m', 'yellow': '\x1b[93m', 'blue': '\x1b[94m', 'purple': '\x1b[95m', 'cyan': '\x1b[96m', 'darkcyan': '\x1b[36m', 'bold': '\x1b[1m', 'underline': '\x1b[4m'}[color.lower()]
except KeyError:
raise ValueError('Unknown ... |
def info(logger, msg, color=None):
msg = ('[{}]'.format(getTime()) + msg)
if (logger is not None):
logger.info(msg)
if (color is not None):
msg = wrapColor(msg, color)
print(msg)
|
def summaryArgs(logger, args, color=None):
if isinstance(args, ModuleType):
args = vars(args)
keys = [key for key in args.keys() if (key[:2] != '__')]
keys.sort()
length = max([len(x) for x in keys])
msg = [(('{:<' + str(length)) + '}: {}').format(k, args[k]) for k in keys]
msg = ('\n'... |
def loadParams(filename):
data = mx.nd.load(filename)
(arg_params, aux_params) = ({}, {})
for (name, value) in data.items():
if (name[:3] == 'arg'):
arg_params[name[4:]] = value
elif (name[:3] == 'aux'):
aux_params[name[4:]] = value
if (len(arg_params) == 0):
... |
class SaveParams(object):
def __init__(self, model, snapshot, model_name, num_save=5):
self.model = model
self.snapshot = snapshot
self.model_name = model_name
self.num_save = num_save
self.save_params = []
def save(self, n_epoch):
self.save_params += [os.path... |
def getLogger(snapshot, model_name):
if (not os.path.exists(snapshot)):
os.makedirs(snapshot)
logging.basicConfig(filename=os.path.join(snapshot, (model_name + '.log')), level=logging.INFO)
logger = logging.getLogger()
return logger
|
class LrScheduler(object):
def __init__(self, method, init_lr, kwargs):
self.method = method
self.init_lr = init_lr
if (method == 'step'):
self.step_list = kwargs['step_list']
self.factor = kwargs['factor']
self.get = self._step
elif (method == ... |
class GradBuffer(object):
def __init__(self, model):
self.model = model
self.cache = None
def write(self):
if (self.cache is None):
self.cache = [[(None if (g is None) else g.copyto(g.context)) for g in g_list] for g_list in self.model._exec_group.grad_arrays]
els... |
def initNormal(mean, std, name, shape):
if name.endswith('_weight'):
return mx.nd.normal(mean, std, shape)
if name.endswith('_bias'):
return mx.nd.zeros(shape)
if name.endswith('_gamma'):
return mx.nd.ones(shape)
if name.endswith('_beta'):
return mx.nd.zeros(shape)
... |
def checkParams(mod, arg_params, aux_params, auto_fix=True, initializer=mx.init.Normal(0.01), logger=None):
arg_params = ({} if (arg_params is None) else arg_params)
aux_params = ({} if (aux_params is None) else aux_params)
arg_shapes = {name: array[0].shape for (name, array) in zip(mod._exec_group.param_... |
def run_eval(data_list, pred_root, gt_root, num_cls):
def compute_confusion_matrix(names, label_root, pred_root, num_cls, num_threads=16, arr_=None):
if (num_threads == 1):
mat = np.zeros((num_cls, num_cls), np.float32)
for name in names:
gt = cv2.imread(os.path.jo... |
def compute_iou(names, num_cls, target_root, gt_root, num_threads=16, arr_=None):
_compute_iou = (lambda x: (np.diag(x) / (((x.sum(axis=0) + x.sum(axis=1)) - np.diag(x)) + 1e-10)))
if isinstance(names, str):
with open(names) as f:
names = [name.strip() for name in f.readlines()]
if (nu... |
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