def make_lstm_ctc(noutput=noutput):
model = nn.Sequential(
layers.Input("BDHW", range=(0, 1), sizes=[None, 1, None, None]),
*combos.conv2d_block(50, 3, mp=(2, 1)),
*combos.conv2d_block(100, 3, mp=(2, 1)),
*combos.conv2d_block(150, 3, mp=2), *project_and_lstm(100, noutput))
flex.shape_inference(model, (1, 1, 128, 512))
return model
def make_lstm_unet(noutput=noutput):
model = nn.Sequential(
layers.Input("BDHW", range=(0, 1), sizes=[None, 1, None, None]),
*combos.conv2d_block(64, 3, repeat=3),
combos.make_unet([64, 128, 256, 512]),
*combos.conv2d_block(128, 3, repeat=2),
*project_and_lstm(100, noutput))
flex.shape_inference(model, (1, 1, 128, 256))
return model
def make_conv_resnet(noutput=noutput):
model = nn.Sequential(
layers.Input("BDHW", range=(0, 1), sizes=[None, 1, None, None]),
*combos.conv2d_block(64, 3, mp=2), *combos.resnet_blocks(5, 64),
*combos.conv2d_block(128, 3, mp=(2, 1)), *combos.resnet_blocks(5, 128),
*combos.conv2d_block(192, 3, mp=2), *combos.resnet_blocks(5, 192),
*combos.conv2d_block(256, 3, mp=(2, 1)), *combos.resnet_blocks(5, 256),
*combos.conv2d_block(512, 3), *project_and_conv1d(512, noutput))
flex.shape_inference(model, (1, 1, 128, 512))
return model
def make_conv_only(noutput=noutput):
model = nn.Sequential(
layers.Input("BDHW", range=(0, 1), sizes=[None, 1, None, None]),
*combos.conv2d_block(100, 3, mp=2, repeat=2),
*combos.conv2d_block(200, 3, mp=2, repeat=2),
*combos.conv2d_block(300, 3, mp=2, repeat=2),
*combos.conv2d_block(400, 3, repeat=2),
*project_and_conv1d(800, noutput))
flex.shape_inference(model, (1, 1, 48, 300))
return model
def make_seg_unet(noutput=3):
model = nn.Sequential(
layers.Input("BDHW", range=(0, 1), sizes=[None, 1, None, None]),
*combos.conv2d_block(64, 3, repeat=3),
combos.make_unet([128, 256, 512]), *combos.conv2d_block(64,
3,
repeat=2),
flex.Conv2d(noutput, 5))
flex.shape_inference(model, (1, 1, 256, 256))
return model
def make_seg_conv(noutput=3):
model = nn.Sequential(
layers.Input("BDHW", range=(0, 1), sizes=[None, 1, None, None]),
layers.KeepSize(
sub=nn.Sequential(*combos.conv2d_block(50, 3, mp=2, repeat=3),
*combos.conv2d_block(100, 3, mp=2, repeat=3),
*combos.conv2d_block(200, 3, mp=2, repeat=3))),
*combos.conv2d_block(400, 5), flex.Conv2d(noutput, 3))
flex.shape_inference(model, (1, 1, 256, 256))
return model
def make_lstm_normalized(noutput=noutput):
model = nn.Sequential(
layers.Input("BDHW", range=(0, 1), sizes=[None, 1, 80, None]),
*combos.conv2d_block(50, 3, mp=(2, 1)),
*combos.conv2d_block(100, 3, mp=(2, 1)),
*combos.conv2d_block(150, 3, mp=2), layers.Reshape(0, [1, 2], 3),
layers.Reorder("BDL", "LBD"), flex.LSTM(100, bidirectional=True),
layers.Reorder("LBD", "BDL"), flex.Conv1d(noutput, 1),
layers.Reorder("BDL", ocr_output))
flex.shape_inference(model, (1, 1, 80, 200))
return model
def make_lstm_transpose(noutput=noutput):
model = nn.Sequential(
layers.Input("BDHW", range=(0, 1), sizes=[None, 1, None, None]),
*combos.conv2d_block(50, 3, repeat=2),
*combos.conv2d_block(100, 3, repeat=2),
*combos.conv2d_block(150, 3, repeat=2),
*combos.conv2d_block(200, 3, repeat=2),
layers.Fun("lambda x: x.sum(2)"), # BDHW -> BDW
flex.ConvTranspose1d(800, 1, stride=2), # <-- undo too tight spacing
#flex.BatchNorm1d(), nn.ReLU(),
layers.Reorder("BDL", "LBD"),
flex.LSTM(100, bidirectional=True),
layers.Reorder("LBD", "BDL"),
flex.Conv1d(noutput, 1),
layers.Reorder("BDL", ocr_output))
flex.shape_inference(model, (1, 1, 128, 512))
return model
def make_lstm_keep(noutput=noutput):
model = nn.Sequential(
layers.Input("BDHW", range=(0, 1), sizes=[None, 1, None, None]),
layers.KeepSize(
mode="nearest",
dims=[3],
sub=nn.Sequential(
*combos.conv2d_block(50, 3, repeat=2),
*combos.conv2d_block(100, 3, repeat=2),
*combos.conv2d_block(150, 3, repeat=2),
layers.Fun("lambda x: x.sum(2)") # BDHW -> BDW
)),
flex.Conv1d(500, 5, padding=2),
flex.BatchNorm1d(),
nn.ReLU(),
layers.Reorder("BDL", "LBD"),
flex.LSTM(200, bidirectional=True),
layers.Reorder("LBD", "BDL"),
flex.Conv1d(noutput, 1),
layers.Reorder("BDL", ocr_output))
flex.shape_inference(model, (1, 1, 128, 512))
return model