def forward(self, inputs, drop_connect_rate=None):
"""
:param inputs: input tensor
:param drop_connect_rate: drop connect rate (float, between 0 and 1)
:return: output of block
"""
# Expansion and Depthwise Convolution
x = inputs
if self._block_args.expand_ratio != 1:
x = relu_fn(self._bn0(self._expand_conv(inputs)))
x = relu_fn(self._bn1(self._depthwise_conv(x)))
# Squeeze and Excitation
if self.has_se:
x_squeezed = F.adaptive_avg_pool2d(x, 1)
x_squeezed = self._se_expand(relu_fn(self._se_reduce(x_squeezed)))
x = torch.sigmoid(x_squeezed) * x
x = self._bn2(self._project_conv(x))
# Skip connection and drop connect
input_filters, output_filters = self._block_args.input_filters, self._block_args.output_filters
if self.id_skip and self._block_args.stride == 1 and input_filters == output_filters:
if drop_connect_rate:
x = drop_connect(x,
p=drop_connect_rate,
training=self.training)
x = x + inputs # skip connection
return x
def extract_features(self, inputs):
""" Returns output of the final convolution layer """
# Stem
###count 21, 29
x = relu_fn(self._bn0(self._conv_stem(inputs)))
#print ("Extract_features fun:: begining", x.shape)
# Blocks
flag = 0
pointer = 0
for idx, block in enumerate(self._blocks):
drop_connect_rate = self._global_params.drop_connect_rate
if drop_connect_rate:
drop_connect_rate *= float(idx) / len(self._blocks)
x = block(x, drop_connect_rate=drop_connect_rate)
#print ("Extract_features fun:: for loop count::", idx , x.shape)
if idx == self.source_layer_indexes[pointer] and flag == 0:
C3 = x
flag = 1
pointer += 1
if idx == self.source_layer_indexes[pointer]:
C4 = x
#if idx==31:
# C5 = x
# Head
x = relu_fn(self._bn1(self._conv_head(x)))
#print ("Extract_features fun:: ending", x.shape)
#return x
return x, C3, C4