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_block_ops(self, all_ops, blocks_args, global_params, input_size, activation, use_bias):
start = True
block_ops = []
block_num = 0
for block_args in blocks_args:
block_args = block_args._replace(
input_filters=round_filters(block_args.input_filters, self.global_params),
output_filters=round_filters(block_args.output_filters, self.global_params),
num_repeat=round_repeats(block_args.num_repeat, self.global_params)
)
if not start:
block_ops.append(self.get_expand_conv(input_size, block_args.input_filters, block_args.input_filters * block_args.expand_ratio, activation, use_bias))
in_channel = block_args.input_filters * block_args.expand_ratio
else:
in_channel = block_args.input_filters
start = False
block_ops, input_size = self._get_efficientnet_ops(block_ops, block_args, input_size, in_channel, activation, use_bias)
all_ops.append(('block_%d' % block_num, block_ops))
block_ops = []
block_num += 1
if block_args.num_repeat > 1:
block_args = block_args._replace(input_filters=block_args.output_filters, stride=1)
in_channel = block_args.input_filters * block_args.expand_ratio
for _ in range(block_args.num_repeat - 1):
block_ops.append(self.get_expand_conv(input_size, block_args.input_filters, block_args.input_filters * block_args.expand_ratio, activation, use_bias))
block_ops, input_size = self._get_efficientnet_ops(block_ops, block_args, input_size, in_channel, activation, use_bias)
all_ops.append(('block_%d' % block_num, block_ops))
block_ops = []
block_num += 1
return all_ops, input_size, block_args.output_filters