def forward(self, inp):
x = self.dense1(inp)
x = self.dense2(x)
x = nn.reshape_4D(x, lowest_dense_res, lowest_dense_res,
self.ae_out_ch)
x = self.upscale1(x)
return x
def forward(self, inp):
x = inp
if 'u' in opts:
x = self.dense_norm(x)
x = self.dense1(x)
x = self.dense2(x)
x = nn.reshape_4D(x, lowest_dense_res, lowest_dense_res,
self.ae_out_ch)
if use_fp16:
x = tf.cast(x, tf.float16)
x = self.upscale1(x)
return x
def forward(self, inp):
x = inp
if 'u' in opts:
x = self.dense_norm(x)
for d in self.dense:
x = d(x)
x = tf.nn.leaky_relu(x, 0.1)
x = nn.reshape_4D(x, lowest_dense_res, lowest_dense_res,
self.ae_out_ch)
x = self.upscale1(x)
return x
def forward(self, inp, stage):
x = inp
for level in range(stage, -1, -1):
if stage in self.enc_blocks:
if level == stage:
x = self.from_rgbs[level](x)
x = self.enc_blocks[level](x)
x = nn.flatten(x)
x = self.dense_norm(x)
x = nn.reshape_4D(x, 1, 1, self.max_ch)
return x
def forward(self, stage, inp, prev_inp=None, alpha=None):
x = inp
for level in range(stage, -1, -1):
if stage in self.from_rgbs:
if level == stage:
x = self.from_rgbs[level](x)
elif level == stage - 1:
x = x * alpha + self.from_rgbs[level](prev_inp) * (
1 - alpha)
if level != 0:
x = self.enc_blocks[level](x)
x = nn.flatten(x)
x = self.dense_norm(x)
x = self.ae_dense1(x)
x = self.ae_dense2(x)
x = nn.reshape_4D(x, ae_res, ae_res, ae_ch)
return x
def forward(self, inp):
x = inp
x = self.dense2(x)
x = nn.reshape_4D (x, lowest_dense_res, lowest_dense_res, self.ae_out_ch)
return x
def forward(self, inp):
x = inp
x = self.dense2(x)
x = nn.reshape_4D (x, inter_res, inter_res, inter_dims)
return x
def forward(self, inp):
x = inp
x = self.conv01(x)
x = x0 = self.conv02(x)
x = self.bp0(x)
x = self.conv11(x)
x = x1 = self.conv12(x)
x = self.bp1(x)
x = self.conv21(x)
x = x2 = self.conv22(x)
x = self.bp2(x)
x = self.conv31(x)
x = self.conv32(x)
x = x3 = self.conv33(x)
x = self.bp3(x)
x = self.conv41(x)
x = self.conv42(x)
x = x4 = self.conv43(x)
x = self.bp4(x)
x = self.conv51(x)
x = self.conv52(x)
x = x5 = self.conv53(x)
x = self.bp5(x)
x = nn.flatten(x)
x = self.dense1(x)
x = self.dense2(x)
x = nn.reshape_4D(x, 4, 4, self.base_ch * 8)
x = self.up5(x)
x = self.uconv53(tf.concat([x, x5], axis=nn.conv2d_ch_axis))
x = self.uconv52(x)
x = self.uconv51(x)
x = self.up4(x)
x = self.uconv43(tf.concat([x, x4], axis=nn.conv2d_ch_axis))
x = self.uconv42(x)
x = self.uconv41(x)
x = self.up3(x)
x = self.uconv33(tf.concat([x, x3], axis=nn.conv2d_ch_axis))
x = self.uconv32(x)
x = self.uconv31(x)
x = self.up2(x)
x = self.uconv22(tf.concat([x, x2], axis=nn.conv2d_ch_axis))
x = self.uconv21(x)
x = self.up1(x)
x = self.uconv12(tf.concat([x, x1], axis=nn.conv2d_ch_axis))
x = self.uconv11(x)
x = self.up0(x)
x = self.uconv02(tf.concat([x, x0], axis=nn.conv2d_ch_axis))
x = self.uconv01(x)
logits = self.out_conv(x)
return logits, tf.nn.sigmoid(logits)
def forward(self, inp, pretrain=False):
x = inp
x = self.conv01(x)
x = x0 = self.conv02(x)
x = self.bp0(x)
x = self.conv11(x)
x = x1 = self.conv12(x)
x = self.bp1(x)
x = self.conv21(x)
x = x2 = self.conv22(x)
x = self.bp2(x)
x = self.conv31(x)
x = self.conv32(x)
x = x3 = self.conv33(x)
x = self.bp3(x)
x = self.conv41(x)
x = self.conv42(x)
x = x4 = self.conv43(x)
x = self.bp4(x)
x = self.conv51(x)
x = self.conv52(x)
x = x5 = self.conv53(x)
x = self.bp5(x)
x = nn.flatten(x)
x = self.dense1(x)
x = self.dense2(x)
x = nn.reshape_4D(x, 4, 4, self.base_ch * 8)
x = self.up5(x)
if pretrain:
x5 = tf.zeros_like(x5)
x = self.uconv53(tf.concat([x, x5], axis=nn.conv2d_ch_axis))
x = self.uconv52(x)
x = self.uconv51(x)
x = self.up4(x)
if pretrain:
x4 = tf.zeros_like(x4)
x = self.uconv43(tf.concat([x, x4], axis=nn.conv2d_ch_axis))
x = self.uconv42(x)
x = self.uconv41(x)
x = self.up3(x)
if pretrain:
x3 = tf.zeros_like(x3)
x = self.uconv33(tf.concat([x, x3], axis=nn.conv2d_ch_axis))
x = self.uconv32(x)
x = self.uconv31(x)
x = self.up2(x)
if pretrain:
x2 = tf.zeros_like(x2)
x = self.uconv22(tf.concat([x, x2], axis=nn.conv2d_ch_axis))
x = self.uconv21(x)
x = self.up1(x)
if pretrain:
x1 = tf.zeros_like(x1)
x = self.uconv12(tf.concat([x, x1], axis=nn.conv2d_ch_axis))
x = self.uconv11(x)
x = self.up0(x)
if pretrain:
x0 = tf.zeros_like(x0)
x = self.uconv02(tf.concat([x, x0], axis=nn.conv2d_ch_axis))
x = self.uconv01(x)
logits = self.out_conv(x)
return logits, tf.nn.sigmoid(logits)
