def __init__(self, block_args, global_params):
super().__init__()
self._block_args = block_args
self._bn_mom = 1 - global_params.batch_norm_momentum
self._bn_eps = global_params.batch_norm_epsilon
self.has_se = (self._block_args.se_ratio
is not None) and (0 < self._block_args.se_ratio <= 1)
self.id_skip = block_args.id_skip # skip connection and drop connect
# Get static or dynamic convolution depending on image size
Conv2d = get_same_padding_conv2d(image_size=global_params.image_size)
# Expansion phase
inp = self._block_args.input_filters # number of input channels
oup = self._block_args.input_filters * self._block_args.expand_ratio # number of output channels
if self._block_args.expand_ratio != 1:
self._expand_conv = Conv2d(in_channels=inp,
out_channels=oup,
kernel_size=1,
bias=False)
self._bn0 = nn.BatchNorm2d(num_features=oup,
momentum=self._bn_mom,
eps=self._bn_eps)
# Depthwise convolution phase
k = self._block_args.kernel_size
s = self._block_args.stride
self._depthwise_conv = Conv2d(
in_channels=oup,
out_channels=oup,
groups=oup, # groups makes it depthwise
kernel_size=k,
stride=s,
bias=False)
self._bn1 = nn.BatchNorm2d(num_features=oup,
momentum=self._bn_mom,
eps=self._bn_eps)
# Squeeze and Excitation layer, if desired
if self.has_se:
num_squeezed_channels = max(
1,
int(self._block_args.input_filters *
self._block_args.se_ratio))
self._se_reduce = Conv2d(in_channels=oup,
out_channels=num_squeezed_channels,
kernel_size=1)
self._se_expand = Conv2d(in_channels=num_squeezed_channels,
out_channels=oup,
kernel_size=1)
# Output phase
final_oup = self._block_args.output_filters
self._project_conv = Conv2d(in_channels=oup,
out_channels=final_oup,
kernel_size=1,
bias=False)
self._bn2 = nn.BatchNorm2d(num_features=final_oup,
momentum=self._bn_mom,
eps=self._bn_eps)
def from_pretrained(cls, model_name, num_classes=1000, in_channels = 3):
model = cls.from_name(model_name, override_params={'num_classes': num_classes})
load_pretrained_weights(model, model_name, load_fc=(num_classes == 1000))
if in_channels != 3:
Conv2d = get_same_padding_conv2d(image_size = model._global_params.image_size)
out_channels = round_filters(32, model._global_params)
model._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False)
return model
def __init__(self, blocks_args=None, global_params=None):
super().__init__()
assert isinstance(blocks_args, list), 'blocks_args should be a list'
assert len(blocks_args) > 0, 'block args must be greater than 0'
self._global_params = global_params
self._blocks_args = blocks_args
# Get static or dynamic convolution depending on image size
Conv2d = get_same_padding_conv2d(image_size=global_params.image_size)
# Batch norm parameters
bn_mom = 1 - self._global_params.batch_norm_momentum
bn_eps = self._global_params.batch_norm_epsilon
# Stem
in_channels = 3 # rgb
out_channels = round_filters(32, self._global_params) # number of output channels
self._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False)
self._bn0 = nn.BatchNorm2d(num_features=out_channels, momentum=bn_mom, eps=bn_eps)
# Build blocks
self._blocks = nn.ModuleList([])
for i in range(len(self._blocks_args)):
# Update block input and output filters based on depth multiplier.
self._blocks_args[i] = self._blocks_args[i]._replace(
input_filters=round_filters(self._blocks_args[i].input_filters, self._global_params),
output_filters=round_filters(self._blocks_args[i].output_filters, self._global_params),
num_repeat=round_repeats(self._blocks_args[i].num_repeat, self._global_params)
)
# The first block needs to take care of stride and filter size increase.
self._blocks.append(MBConvBlock(self._blocks_args[i], self._global_params))
if self._blocks_args[i].num_repeat > 1:
self._blocks_args[i] = self._blocks_args[i]._replace(input_filters=self._blocks_args[i].output_filters, stride=1)
for _ in range(self._blocks_args[i].num_repeat - 1):
self._blocks.append(MBConvBlock(self._blocks_args[i], self._global_params))
# Head'efficientdet-d0': 'efficientnet-b0',
in_channels = self._blocks_args[len(self._blocks_args)-1].output_filters # output of final block
out_channels = round_filters(1280, self._global_params)
self._conv_head = Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
self._bn1 = nn.BatchNorm2d(num_features=out_channels, momentum=bn_mom, eps=bn_eps)
# Final linear layer
self._avg_pooling = nn.AdaptiveAvgPool2d(1)
self._dropout = nn.Dropout(self._global_params.dropout_rate)
self._fc = nn.Linear(out_channels, self._global_params.num_classes)
self._swish = MemoryEfficientSwish()
def get_network_from_name(cls,
model_name,
num_classes=1000,
in_channels=3):
model = cls.from_name(model_name,
override_params={'num_classes': num_classes})
if in_channels != 3:
Conv2d = get_same_padding_conv2d(
image_size=model._global_params.image_size)
out_channels = round_filters(32, model._global_params)
model._conv_stem = Conv2d(in_channels,
out_channels,
kernel_size=3,
stride=2,
bias=False)
return model
