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model_coat_daformer_unet.py
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from kaggle_hubmap_kv3 import *
from daformer import *
from coat import *
import copy
from segmentation_models_pytorch.decoders.unet.decoder import UnetDecoder
# import lovasz_losses as L
#################################################################
def criterion_aux_loss(logit, mask):
mask = F.interpolate(mask,size=logit.shape[-2:], mode='nearest')
loss = F.binary_cross_entropy_with_logits(logit,mask)
return loss
class RGB(nn.Module):
IMAGE_RGB_MEAN = [0.485, 0.456, 0.406] # [0.5, 0.5, 0.5]
IMAGE_RGB_STD = [0.229, 0.224, 0.225] # [0.5, 0.5, 0.5]
def __init__(self, ):
super(RGB, self).__init__()
self.register_buffer('mean', torch.zeros(1, 3, 1, 1))
self.register_buffer('std', torch.ones(1, 3, 1, 1))
self.mean.data = torch.FloatTensor(self.IMAGE_RGB_MEAN).view(self.mean.shape)
self.std.data = torch.FloatTensor(self.IMAGE_RGB_STD).view(self.std.shape)
def forward(self, x):
x = (x - self.mean) / self.std
return x
class Net(nn.Module):
def __init__(self,
encoder=coat_lite_medium,
decoder=daformer_conv3x3,
encoder_cfg={},
decoder_cfg={},
encoder_ckpt=None,
decoder_ckpt=None
):
super(Net, self).__init__()
decoder_dim = decoder_cfg.get('decoder_dim', 320)
decoder_dim_unet = [256, 128, 64, 32, 16]
# ----
self.rgb = RGB()
conv_dim = 32
self.conv = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.Conv2d(32, conv_dim, kernel_size=3, stride=1, padding=1, bias=False)
)
self.convs = torch.nn.ModuleList()
self.convs.append(self.conv)
self.convs.append(copy.deepcopy(self.convs[0]))
self.encoders_coat = torch.nn.ModuleList()
self.encoders_coat.append(encoder())
if encoder_ckpt is not None:
checkpoint = torch.load(encoder_ckpt, map_location=lambda storage, loc: storage)
self.encoders_coat[0].load_state_dict(checkpoint['model'],strict=False)
self.encoders_coat.append(copy.deepcopy(self.encoders_coat[0]))
encoder_dim = self.encoders_coat[0].embed_dims
self.decoders_daformer = torch.nn.ModuleList()
self.decoders_daformer.append(decoder(
encoder_dim=encoder_dim,
decoder_dim=decoder_dim,
))
self.decoders_daformer.append(copy.deepcopy(self.decoders_daformer[0]))
self.decoders_unet = torch.nn.ModuleList()
self.decoders_unet.append(
UnetDecoder(
encoder_channels=[0, conv_dim] + encoder_dim,
decoder_channels=decoder_dim_unet,
n_blocks=5,
use_batchnorm=True,
center=False,
attention_type=None,
))
self.decoders_unet.append(copy.deepcopy(self.decoders_unet[0]))
self.logit = nn.Sequential(
nn.Conv2d(decoder_dim, 1, kernel_size=1),
)
self.logit_unet = nn.Sequential(
nn.Conv2d(decoder_dim_unet[-1], 1, kernel_size=1),
)
self.output_type = ['inference', 'loss']
def forward(self, batch):
# import ipdb;ipdb.set_trace()
# mask = batch['mask']
organs = batch['organ']
x = batch['image']
x = self.rgb(x)
B, C, H, W = x.shape
encoder = self.encoders_coat[organs[0].item() // 4](x)
last_daformer, decoder = self.decoders_daformer[organs[0].item() // 4](encoder)
logit_daformer = self.logit(last_daformer)
logit_daformer = F.interpolate(logit_daformer, size=None, scale_factor=4, mode='bilinear', align_corners=False)
conv = self.convs[organs[0].item() // 4](x)
feature = encoder[::-1]
head = feature[0]
skip = feature[1:] + [conv, None]
d = self.decoders_unet[organs[0].item() // 4].center(head)
decoder = []
for i, decoder_block in enumerate(self.decoders_unet[organs[0].item() // 4].blocks):
s = skip[i]
d = decoder_block(d, s)
decoder.append(d)
last_unet = d
logit_unet = self.logit_unet(last_unet)
logit = (logit_daformer + logit_unet) / 2
# mask = F.interpolate(mask, size=None, scale_factor=1/4, mode='bilinear', align_corners=False)
output = {}
if 'loss' in self.output_type:
mask = batch['mask']
# mask = F.interpolate(mask, size=None, scale_factor=1/4, mode='bilinear', align_corners=False)
output['bce_loss'] = F.binary_cross_entropy_with_logits(logit, mask)
if 'inference' in self.output_type:
output['probability'] = torch.sigmoid(logit)
return output
def run_check_net():
batch_size = 2
image_size = 800
# ---
batch = {
'image': torch.from_numpy(np.random.uniform(-1, 1, (batch_size, 3, image_size, image_size))).float(),
'mask': torch.from_numpy(np.random.choice(2, (batch_size, 1, image_size, image_size))).float(),
'organ': torch.from_numpy(np.random.choice(5, (batch_size, 1))).long(),
}
batch = {k: v.cuda() for k, v in batch.items()}
net = Net().cuda()
with torch.no_grad():
with torch.cuda.amp.autocast(enabled=True):
output = net(batch)
print('batch')
for k, v in batch.items():
print('%32s :' % k, v.shape)
print('output')
for k, v in output.items():
if 'loss' not in k:
print('%32s :' % k, v.shape)
for k, v in output.items():
if 'loss' in k:
print('%32s :' % k, v.item())
# main #################################################################
if __name__ == '__main__':
run_check_net()