models.py

'''
Created on Sep 3, 2017

@author: Michal.Busta at gmail.com
'''

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np

from torch.nn import LeakyReLU, Conv2d, Dropout2d, LogSoftmax, InstanceNorm2d

import math    

class CReLU(nn.Module):
  def __init__(self):
    super(CReLU, self).__init__()
  def forward(self, x):
    return torch.cat((F.leaky_relu(x, 0.01, inplace=True), F.leaky_relu(-x, 0.01, inplace=True)), 1)

class CReLU_IN(nn.Module):
  def __init__(self, channels):
    super(CReLU_IN, self).__init__()
    self.bn = nn.InstanceNorm2d(channels * 2, eps=1e-05, momentum=0.1, affine=True)
  def forward(self, x):
    cat = torch.cat((x, -x), 1)
    x = self.bn(cat)
    return F.leaky_relu(x, 0.01, inplace=True)



def conv_bn(inp, oup, stride):
    return nn.Sequential(
      nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
      nn.BatchNorm2d(oup),
      nn.ReLU(inplace=True)
    )

def conv_dw(inp, oup, stride, dilation=1):
  return nn.Sequential(
    nn.Conv2d(inp, inp, 3, stride, 1 + (dilation > 0) * (dilation -1), dilation=dilation, groups=inp, bias=False),
    nn.BatchNorm2d(inp),
    nn.LeakyReLU(inplace=True, negative_slope=0.01),

    nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
    nn.BatchNorm2d(oup),
    nn.LeakyReLU(inplace=True, negative_slope=0.01),
  )
  
def conv_dw_plain(inp, oup, stride, dilation=1):
  return nn.Sequential(
    nn.Conv2d(inp, inp, 3, stride, 1 + (dilation > 0) * (dilation -1), dilation=dilation, groups=inp, bias=False),
    nn.Conv2d(inp, oup, 1, 1, 0, bias=False)
  )
  
def conv_dw_res(inp, oup, stride):
  return nn.Sequential(
    nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
    nn.BatchNorm2d(inp),
    nn.LeakyReLU(inplace=True, negative_slope=0.01),

    nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
    nn.BatchNorm2d(oup),
  )

def conv_dw_in(inp, oup, stride, dilation=1):
  return nn.Sequential(
    nn.Conv2d(inp, inp, 3, stride, 1 + (dilation > 0) * (dilation -1), dilation=dilation, groups=inp, bias=False),
    nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
    InstanceNorm2d(oup, eps=1e-05, momentum=0.1),
    nn.LeakyReLU(inplace=True, negative_slope=0.01),
  )

def conv_dw_res_in(inp, oup, stride):
  return nn.Sequential(
    nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
    nn.InstanceNorm2d(inp, eps=1e-05, momentum=0.1, affine=True),
    nn.LeakyReLU(inplace=True, negative_slope=0.01),

    nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
    nn.InstanceNorm2d(oup, eps=1e-05, momentum=0.1, affine=True)
  )
    
def dice_loss(inp, target):
    
  smooth = 1.
  iflat = inp.view(-1)
  tflat = target.view(-1)
  intersection = (iflat * tflat).sum()
  
  return - ((2. * intersection + smooth) /
            (iflat.sum() + tflat.sum() + smooth))

class BasicBlockSep(nn.Module):
  expansion = 1
  def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):
    super(BasicBlockSep, self).__init__()
    self.conv_sep1 = conv_dw(inplanes, planes, stride, dilation=dilation)
    self.conv2 = conv_dw_res(planes, planes, 1)
    self.downsample = downsample
    self.stride = stride
    self.relu = LeakyReLU(negative_slope=0.01, inplace=True)

  def forward(self, x):
    residual = x

    out = self.conv_sep1(x)

    out = self.conv2(out)

    if self.downsample is not None:
      residual = self.downsample(x)

    out += residual
    out = self.relu(out)

    return out  
      
class BasicBlockIn(nn.Module):
  expansion = 1
  def __init__(self, inplanes, planes, stride=1, downsample=None):
    super(BasicBlockIn, self).__init__()
    self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
    self.bn1 = InstanceNorm2d(planes, eps=1e-05, momentum=0.1, affine=True)
    self.relu = nn.ReLU(inplace=True)
    self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
    self.bn2 = InstanceNorm2d(planes, eps=1e-05, momentum=0.1, affine=True)
    self.downsample = downsample
    self.stride = stride

  def forward(self, x):
    residual = x

    out = self.conv1(x)
    out = self.bn1(out)
    out = self.relu(out)

    out = self.conv2(out)
    out = self.bn2(out)

    if self.downsample is not None:
        residual = self.downsample(x)

    out += residual
    out = self.relu(out)

    return out
  
class BasicBlockSepIn(nn.Module):
  expansion = 1

  def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):
    super(BasicBlockSepIn, self).__init__()
    
    self.conv_sep1 = conv_dw_in(inplanes, planes, stride, dilation=dilation)
    self.conv2 = conv_dw_res_in(planes, planes, 1)
    self.downsample = downsample
    self.stride = stride
    self.relu = LeakyReLU(negative_slope=0.01, inplace=True)

  def forward(self, x):
    residual = x

    out = self.conv_sep1(x)

    out = self.conv2(out)

    if self.downsample is not None:
      residual = self.downsample(x)

    out += residual
    out = self.relu(out)

    return out  
  
def iou_loss(roi_gt, byte_mask, roi_pred, box_loss_value):
  d1_gt = roi_gt[:, :, :, 0][byte_mask]
  d2_gt = roi_gt[:, :, :, 1][byte_mask] 
  d3_gt = roi_gt[:, :, :, 2][byte_mask]
  d4_gt = roi_gt[:, :, :, 3][byte_mask] 
  
  mask3 = torch.gt(d3_gt, 0)   
  mask4 = torch.gt(d4_gt, 0)   
  d3_gt = d3_gt[mask3]
  d4_gt = d4_gt[mask4] 
  
  
  d1_pred = roi_pred[:, 0, :, :][byte_mask]
  d2_pred = roi_pred[:, 1, :, :][byte_mask]
  d3_pred = roi_pred[:, 2, :, :][byte_mask]
  d3_pred = d3_pred[mask3]
  d4_pred = roi_pred[:, 3, :, :][byte_mask]
  d4_pred = d4_pred[mask4]
  
  area_gt_l = (d1_gt[mask3] + d2_gt[mask3]) * (d3_gt)
  area_pred_l = (d1_pred[mask3] + d2_pred[mask3]) * (d3_pred)
  w_union_l = torch.min(d3_gt, d3_pred)
  h_union_l = torch.min(d1_gt[mask3], d1_pred[mask3]) + torch.min(d2_gt[mask3], d2_pred[mask3])
  area_intersect_l = w_union_l * h_union_l
  area_union_l = area_gt_l + area_pred_l - area_intersect_l
  AABB_l = - torch.log((area_intersect_l + 1.0)/(area_union_l + 1.0))
  
  if AABB_l.dim() > 0:
    box_loss_value += torch.mean(AABB_l)
  
  area_gt_r = (d1_gt[mask4] + d2_gt[mask4]) * (d4_gt)
  area_pred_r = (d1_pred[mask4] + d2_pred[mask4]) * (d4_pred)
  w_union_r = torch.min(d4_gt, d4_pred)
  h_union_r = torch.min(d1_gt[mask4], d1_pred[mask4]) + torch.min(d2_gt[mask4], d2_pred[mask4])
  area_intersect_r = w_union_r * h_union_r
  area_union_r = area_gt_r + area_pred_r - area_intersect_r
  AABB_r = - torch.log((area_intersect_r + 1.0)/(area_union_r + 1.0))
  if AABB_r.dim() > 0:
    box_loss_value += torch.mean(AABB_r)
  
class ModelResNetSep2(nn.Module):
  
  def recompute(self):
    self.layer0[0].recompute_weights()
    self.layer0[2].recompute_weights()
    self.layer0_1[0].recompute_weights()
    self.layer0_1[2].recompute_weights()
            
  def __init__(self, attention = False, multi_scale = True):
    super(ModelResNetSep2, self).__init__()
    
    self.inplanes = 64
    
    self.layer0 = nn.Sequential(
      Conv2d(3, 16, 3, stride=1, padding=1, bias=False),
      CReLU_IN(16),
      Conv2d(32, 32, 3, stride=2, padding=1, bias=False),
      CReLU_IN(32)
    )
    
    self.layer0_1 = nn.Sequential(
      Conv2d(64, 64, 3, stride=1, padding=1, bias=False),
      #nn.InstanceNorm2d(64, affine=True),
      nn.ReLU(),
      Conv2d(64, 64, 3, stride=2, padding=1, bias=False),
      #nn.InstanceNorm2d(64, affine=True),
      nn.ReLU(inplace=True)
    )
    
    self.conv5 = Conv2d(64, 128, (3,3), padding=(1, 1), bias=False)
    self.conv6 = Conv2d(128, 128, (3,3), padding=1, bias=False)
    self.conv7 = Conv2d(128,256, 3, padding=1, bias=False)
    self.conv8 = Conv2d(256, 256, (3,3), padding=1, bias=False)
    self.conv9 = Conv2d(256, 256, (3,3), padding=(1, 1), bias=False)
    self.conv10_s = Conv2d(256, 256, (2, 3), padding=(0, 1), bias=False)
    self.conv11 = Conv2d(256, 8400, (1, 1), padding=(0,0))
    
    self.batch5 = InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
    self.batch6 = InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
    self.batch7 = InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
    self.batch8 = InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
    self.batch9 = InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
    self.batch10_s = InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
    self.max2 = nn.MaxPool2d((2, 1), stride=(2,1))
    self.leaky = LeakyReLU(negative_slope=0.01, inplace=True)
    
    self.layer1 = self._make_layer(BasicBlockIn, 64, 3, stride=1)
    self.inplanes = 64
    self.layer2 = self._make_layer(BasicBlockIn, 128, 4, stride=2)
    self.layer3 = self._make_layer(BasicBlockSepIn, 256, 6, stride=2)
    self.layer4 = self._make_layer(BasicBlockSepIn, 512, 4, stride=2)
    
    self.feature4 = nn.Conv2d(512, 256, 1, stride=1, padding=0, bias=False)
    self.feature3 = nn.Conv2d(256, 256, 1, stride=1, padding=0, bias=False)
    self.feature2 = nn.Conv2d(128, 256, 1, stride=1, padding=0, bias=False)
    
    self.upconv2 = conv_dw_plain(256, 256, stride=1)
    self.upconv1 = conv_dw_plain(256, 256, stride=1)
    
    self.feature1 = nn.Conv2d(64, 256, 1, stride=1, padding=0, bias=False)
    
    self.act = Conv2d(256, 1, (1,1), padding=0, stride=1)
    self.rbox = Conv2d(256, 4, (1,1), padding=0, stride=1)
    
    self.angle = Conv2d(256, 2, (1,1), padding=0, stride=1)
    self.drop1 = Dropout2d(p=0.2, inplace=False)
    
    self.angle_loss = nn.MSELoss(reduction='elementwise_mean')
    self.h_loss = nn.SmoothL1Loss(reduction='elementwise_mean')
    self.w_loss = nn.SmoothL1Loss(reduction='elementwise_mean')
    
    self.attention = attention
  
    if self.attention:
      self.conv_attenton = nn.Conv2d(256, 1, kernel_size=1, stride=1, padding=0, bias=True) 
    
    self.multi_scale = multi_scale
  
  def _make_layer(self, block, planes, blocks, stride=1):
    
    downsample = None
    if stride != 1 or self.inplanes != planes * block.expansion:
      downsample = nn.Sequential(
  
        nn.Conv2d(self.inplanes, planes * block.expansion,
                  kernel_size=1, stride=stride, bias=False),
        nn.BatchNorm2d(planes * block.expansion),
      )

    layers = []
    layers.append(block(self.inplanes, planes, stride, downsample))
    self.inplanes = planes * block.expansion
    for i in range(1, blocks):
      layers.append(block(self.inplanes, planes))

    return nn.Sequential(*layers)
  
  def forward_ocr(self, x):
    
    x = self.conv5(x)
    x = self.batch5(x)
    x = self.leaky(x)
    
    x = self.conv6(x)
    x = self.leaky(x)
    x = self.conv6(x)
    x = self.leaky(x)
    
    x = self.max2(x)
    x = self.conv7(x)
    x = self.batch7(x)
    x = self.leaky(x)
    
    
    x = self.conv8(x)
    x = self.leaky(x)
    x = self.conv8(x)
    x = self.leaky(x)
    
    x = self.conv9(x)
    x = self.leaky(x)
    x = self.conv9(x)
    x = self.leaky(x)
    
    x = self.max2(x)
    
    x = self.conv10_s(x)
    x = self.batch10_s(x)
    x = self.leaky(x)
    
    
    x = self.drop1(x)
    x = self.conv11(x)
    x = x.squeeze(2)

    x = x.permute(0,2,1)
    y = x
    x = x.contiguous().view(-1,x.data.shape[2])
    x = LogSoftmax(len(x.size()) - 1)(x)
    x = x.view_as(y)
    x = x.permute(0,2,1)
    
    return x   
  
  def forward_features(self, x):
    
    x = self.layer0(x)
    focr = self.layer0_1(x)
    return focr

  def forward(self, x):
    
    x = self.layer0(x)
    x = self.layer0_1(x)
    
    x = self.drop1(x)
    su3 = self.layer1(x)
    features1 = self.feature1(su3)
    su2 = self.layer2(su3)
    features2 = self.feature2(su2)
    su1 = self.layer3(su2)
    features3 = self.feature3(su1)
    x = self.layer4(su1)
    
    x = self.drop1(x)
    
    features4 = self.feature4(x)
    if self.attention:
      att = self.conv_attenton(features4)
      att = torch.sigmoid(att)
      att = att.expand_as(features4)
      att_up = F.interpolate(att, size=(features3.size(2), features3.size(3)), mode='bilinear', align_corners=True)
    
    x = F.interpolate(features4, size=(features3.size(2), features3.size(3)), mode='bilinear', align_corners=True)
    
    if self.attention:
      x = x + features3 * att_up
      att = self.conv_attenton(x)
      att = torch.sigmoid(att)
      att_up = F.interpolate(att, size=(features2.size(2), features2.size(3)), mode='bilinear', align_corners=True)
    else:
      x = x + features3 
      
    x = F.interpolate(x, size=(features2.size(2), features2.size(3)), mode='bilinear', align_corners=True)
    x = self.upconv1(x)
    if self.attention:
      features2 = x + features2 * att_up
      att = self.conv_attenton(features2)
      att = torch.sigmoid(att)
      att_up = F.interpolate(att, size=(features1.size(2), features1.size(3)), mode='bilinear', align_corners=True)
    else:
      features2 = x + features2  
    x = features2
        
    x = F.interpolate(x, size=(features1.size(2), features1.size(3)), mode='bilinear', align_corners=True)
    x = self.upconv2(x)
    
    if self.attention:
      x = x + features1 * att_up
    else:
      x += features1
    
    segm_pred2 = torch.sigmoid(self.act(features2))
    rbox2 = torch.sigmoid(self.rbox(features2)) * 128
    angle2 = torch.sigmoid(self.angle(features2)) * 2 - 1 
    angle_den = torch.sqrt(angle2[:, 0, :, :] * angle2[:, 0, :, :] + angle2[:, 1, :, :] * angle2[:, 1, :, :]).unsqueeze(1)
    angle_den = angle_den.expand_as(angle2)
    angle2 = angle2 / angle_den
    
    x = self.drop1(x)
    
    segm_pred = torch.sigmoid(self.act(x))
    rbox = torch.sigmoid(self.rbox(x)) * 128
    angle = torch.sigmoid(self.angle(x)) * 2 - 1 
    angle_den = torch.sqrt(angle[:, 0, :, :] * angle[:, 0, :, :] + angle[:, 1, :, :] * angle[:, 1, :, :]).unsqueeze(1)
    angle_den = angle_den.expand_as(angle)
    angle = angle / angle_den
    
    return [segm_pred, segm_pred2], [rbox, rbox2], [angle, angle2], x

  def loss(self, segm_preds, segm_gt, iou_mask, angle_preds, angle_gt, roi_pred, roi_gt):
    
    self.box_loss_value =  torch.tensor(0.0, requires_grad = True).cuda()
    self.angle_loss_value =  torch.tensor(0.0, requires_grad = True).cuda()
  
    segm_pred = segm_preds[0].squeeze(1)
    angle_pred = angle_preds[0]
    self.segm_loss_value = dice_loss(segm_pred * iou_mask , segm_gt * iou_mask )
    segm_pred1 = segm_preds[1].squeeze(1)
    
    if self.multi_scale:
      iou_gts = F.interpolate(segm_gt.unsqueeze(1), size=(segm_pred1.size(1), segm_pred1.size(2)), mode='bilinear', align_corners=True).squeeze(1)
      iou_masks = F.interpolate(iou_mask.unsqueeze(1), size=(segm_pred1.size(1), segm_pred1.size(2)), mode='bilinear', align_corners=True).squeeze(1)
      self.segm_loss_value += dice_loss(segm_pred1 * iou_masks, iou_gts * iou_masks )
      
    byte_mask = torch.gt(segm_gt, 0.5)
    
    if byte_mask.sum() > 0:
      
      gt_sin = torch.sin(angle_gt[byte_mask])
      gt_cos = torch.cos(angle_gt[byte_mask])
      
      sin_val = self.angle_loss(angle_pred[:, 0, :, :][byte_mask], gt_sin)
      cos_val = self.angle_loss(angle_pred[:, 1, :, :][byte_mask], gt_cos)
       
      self.angle_loss_value += sin_val
      self.angle_loss_value += cos_val
      
      iou_loss(roi_gt, byte_mask, roi_pred[0], self.box_loss_value)
        
      if self.multi_scale:
        byte_mask = torch.gt(F.interpolate(segm_gt.unsqueeze(1), size=(segm_pred1.size(1), segm_pred1.size(2)), mode='bilinear', align_corners=True), 0.5).squeeze(1)
        if byte_mask.sum() > 0:
          
          angle_gts = F.interpolate(angle_gt.unsqueeze(1), size=(segm_pred1.size(1), segm_pred1.size(2)), mode='bilinear', align_corners=True).squeeze(1)
          gt_sin = torch.sin(angle_gts[byte_mask])
          gt_cos = torch.cos(angle_gts[byte_mask])
          sin_val = self.angle_loss(angle_preds[1][:, 0, :, :][byte_mask], gt_sin)
          
          self.angle_loss_value += sin_val
          self.angle_loss_value += self.angle_loss(angle_preds[1][:, 1, :, :][byte_mask], gt_cos)
          
          roi_gt_s = F.interpolate(roi_gt.permute(0, 3, 1, 2), size=(segm_pred1.size(1), segm_pred1.size(2)), mode='bilinear', align_corners=True) / 2
          roi_gt_s = roi_gt_s.permute(0, 2, 3, 1)
          iou_loss(roi_gt_s, byte_mask, roi_pred[1], self.box_loss_value)
            
    return self.segm_loss_value +  self.angle_loss_value * 2 + 0.5 * self.box_loss_value  
  
class ModelMLTRCTW(nn.Module):
  
  def recompute(self):
    self.layer0[0].recompute_weights()
    self.layer0[2].recompute_weights()
    self.layer0_1[0].recompute_weights()
    self.layer0_1[2].recompute_weights()
            
  def __init__(self, attention = False, multi_scale = True):
    super(ModelMLTRCTW, self).__init__()
    
    self.inplanes = 64
    
    self.layer0 = nn.Sequential(
      Conv2d(3, 16, 3, stride=1, padding=1, bias=False),
      CReLU_IN(16),
      Conv2d(32, 32, 3, stride=2, padding=1, bias=False),
      CReLU_IN(32)
    )
    
    self.layer0_1 = nn.Sequential(
      Conv2d(64, 64, 3, stride=1, padding=1, bias=False),
      #nn.InstanceNorm2d(64, affine=True),
      nn.ReLU(),
      Conv2d(64, 64, 3, stride=2, padding=1, bias=False),
      #nn.InstanceNorm2d(64, affine=True),
      nn.ReLU(inplace=True)
    )
    
    self.conv5 = Conv2d(64, 128, (3,3), padding=(1, 1), bias=False)
    self.conv6 = Conv2d(128, 128, (3,3), padding=1, bias=False)
    self.conv7 = Conv2d(128,256, 3, padding=1, bias=False)
    self.conv8 = Conv2d(256, 256, (3,3), padding=1, bias=False)
    self.conv9 = Conv2d(256, 256, (3,3), padding=(1, 1), bias=False)
    self.conv10_s = Conv2d(256, 256, (2, 3), padding=(0, 1), bias=False)
    self.conv11 = Conv2d(256, 8400, (1, 1), padding=(0,0))
    
    self.batch5 = InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
    self.batch6 = InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
    self.batch7 = InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
    self.batch8 = InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
    self.batch9 = InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
    self.batch10_s = InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
    self.max2 = nn.MaxPool2d((2, 1), stride=(2,1))
    self.leaky = LeakyReLU(negative_slope=0.01, inplace=True)
    
    self.layer1 = self._make_layer(BasicBlockIn, 64, 3, stride=1)
    self.inplanes = 64
    self.layer2 = self._make_layer(BasicBlockIn, 128, 4, stride=2)
    self.layer3 = self._make_layer(BasicBlockSepIn, 256, 6, stride=2)
    self.layer4 = self._make_layer(BasicBlockSepIn, 512, 4, stride=2)
    
    self.feature4 = nn.Conv2d(512, 256, 1, stride=1, padding=0, bias=False)
    self.feature3 = nn.Conv2d(256, 256, 1, stride=1, padding=0, bias=False)
    self.feature2 = nn.Conv2d(128, 256, 1, stride=1, padding=0, bias=False)
    
    self.upconv2 = conv_dw_plain(256, 256, stride=1)
    self.upconv1 = conv_dw_plain(256, 256, stride=1)
    
    self.feature1 = nn.Conv2d(64, 256, 1, stride=1, padding=0, bias=False)
    
    self.act = Conv2d(256, 1, (1,1), padding=0, stride=1)
    self.rbox = Conv2d(256, 4, (1,1), padding=0, stride=1)
    
    self.angle = Conv2d(256, 2, (1,1), padding=0, stride=1)
    self.drop1 = Dropout2d(p=0.2, inplace=False)
    
    self.angle_loss = nn.MSELoss(reduction='elementwise_mean')
    self.h_loss = nn.SmoothL1Loss(reduction='elementwise_mean')
    self.w_loss = nn.SmoothL1Loss(reduction='elementwise_mean')
    
    self.attention = attention
  
    if self.attention:
      self.conv_attenton = nn.Conv2d(256, 1, kernel_size=1, stride=1, padding=0, bias=True) 
    
    self.multi_scale = multi_scale
    
  def copy_ocr(self):
    import copy
    self.layer0o = copy.deepcopy(self.layer0)
    self.layer0_1o = copy.deepcopy(self.layer0_1)
  
  def _make_layer(self, block, planes, blocks, stride=1):
    
    downsample = None
    if stride != 1 or self.inplanes != planes * block.expansion:
      downsample = nn.Sequential(
  
        nn.Conv2d(self.inplanes, planes * block.expansion,
                  kernel_size=1, stride=stride, bias=False),
        nn.BatchNorm2d(planes * block.expansion),
      )

    layers = []
    layers.append(block(self.inplanes, planes, stride, downsample))
    self.inplanes = planes * block.expansion
    for i in range(1, blocks):
      layers.append(block(self.inplanes, planes))

    return nn.Sequential(*layers)
  
  def forward_ocr(self, x):
    
    x = self.conv5(x)
    x = self.batch5(x)
    x = self.leaky(x)
    
    x = self.conv6(x)
    x = self.leaky(x)
    x = self.conv6(x)
    x = self.leaky(x)
    
    x = self.max2(x)
    x = self.conv7(x)
    x = self.batch7(x)
    x = self.leaky(x)
    
    x = self.conv8(x)
    x = self.leaky(x)
    x = self.conv8(x)
    x = self.leaky(x)
    
    x = self.conv9(x)
    x = self.leaky(x)
    x = self.conv9(x)
    x = self.leaky(x)
    
    x = self.max2(x)
    
    x = self.conv10_s(x)
    x = self.batch10_s(x)
    x = self.leaky(x)
    
    
    x = self.drop1(x)
    x = self.conv11(x)
    x = x.squeeze(2)

    x = x.permute(0,2,1)
    y = x
    x = x.contiguous().view(-1,x.data.shape[2])
    x = LogSoftmax(len(x.size()) - 1)(x)
    x = x.view_as(y)
    x = x.permute(0,2,1)
    
    return x   
  
  def forward_features(self, x):
    
    x = self.layer0(x)
    x = self.layer0_1(x)
    return x

  def forward(self, x):
    
    x = self.layer0(x)
    x = self.layer0_1(x)
    
    x = self.drop1(x)
    su3 = self.layer1(x)
    features1 = self.feature1(su3)
    su2 = self.layer2(su3)
    features2 = self.feature2(su2)
    su1 = self.layer3(su2)
    features3 = self.feature3(su1)
    x = self.layer4(su1)
    
    x = self.drop1(x)
    
    features4 = self.feature4(x)
    if self.attention:
      att = self.conv_attenton(features4)
      att = torch.sigmoid(att)
      att = att.expand_as(features4)
      att_up = F.interpolate(att, size=(features3.size(2), features3.size(3)), mode='bilinear', align_corners=True)
    
    x = F.interpolate(features4, size=(features3.size(2), features3.size(3)), mode='bilinear', align_corners=True)
    
    if self.attention:
      x = x + features3 * att_up
      att = self.conv_attenton(x)
      att = torch.sigmoid(att)
      att_up = F.interpolate(att, size=(features2.size(2), features2.size(3)), mode='bilinear', align_corners=True)
    else:
      x = x + features3 
      
    x = F.interpolate(x, size=(features2.size(2), features2.size(3)), mode='bilinear', align_corners=True)
    x = self.upconv1(x)
    if self.attention:
      features2 = x + features2 * att_up
      att = self.conv_attenton(features2)
      att = torch.sigmoid(att)
      att_up = F.interpolate(att, size=(features1.size(2), features1.size(3)), mode='bilinear', align_corners=True)
    else:
      features2 = x + features2  
    x = features2
        
    x = F.interpolate(x, size=(features1.size(2), features1.size(3)), mode='bilinear', align_corners=True)
    x = self.upconv2(x)
    
    if self.attention:
      x = x + features1 * att_up
    else:
      x += features1
    
    segm_pred2 = torch.sigmoid(self.act(features2))
    rbox2 = torch.sigmoid(self.rbox(features2)) * 128
    angle2 = torch.sigmoid(self.angle(features2)) * 2 - 1 
    angle_den = torch.sqrt(angle2[:, 0, :, :] * angle2[:, 0, :, :] + angle2[:, 1, :, :] * angle2[:, 1, :, :]).unsqueeze(1)
    angle_den = angle_den.expand_as(angle2)
    angle2 = angle2 / angle_den
    
    x = self.drop1(x)
    
    segm_pred = torch.sigmoid(self.act(x))
    rbox = torch.sigmoid(self.rbox(x)) * 128
    angle = torch.sigmoid(self.angle(x)) * 2 - 1 
    angle_den = torch.sqrt(angle[:, 0, :, :] * angle[:, 0, :, :] + angle[:, 1, :, :] * angle[:, 1, :, :]).unsqueeze(1)
    angle_den = angle_den.expand_as(angle)
    angle = angle / angle_den
    
    return [segm_pred, segm_pred2], [rbox, rbox2], [angle, angle2], x
    

  def loss(self, segm_preds, segm_gt, iou_mask, angle_preds, angle_gt, roi_pred, roi_gt):
    
    self.box_loss_value =  torch.tensor(0.0, requires_grad = True).cuda()
    self.angle_loss_value =  torch.tensor(0.0, requires_grad = True).cuda()
  
    segm_pred = segm_preds[0].squeeze(1)
    angle_pred = angle_preds[0]
    self.iou_loss_value = dice_loss(segm_pred * iou_mask , segm_gt * iou_mask )
    segm_pred1 = segm_preds[1].squeeze(1)
    
    if self.multi_scale:
      iou_gts = F.interpolate(segm_gt.unsqueeze(1), size=(segm_pred1.size(1), segm_pred1.size(2)), mode='bilinear', align_corners=True).squeeze(1)
      iou_masks = F.interpolate(iou_mask.unsqueeze(1), size=(segm_pred1.size(1), segm_pred1.size(2)), mode='bilinear', align_corners=True).squeeze(1)
      self.iou_loss_value += dice_loss(segm_pred1 * iou_masks, iou_gts * iou_masks )
      
    
    masked_segm = segm_gt.data
    byte_mask = torch.gt(masked_segm, 0.5)
    
    if byte_mask.sum() > 0:
      
      gt_sin = torch.sin(angle_gt[byte_mask])
      gt_cos = torch.cos(angle_gt[byte_mask])
      
      sin_val = self.angle_loss(angle_pred[:, 0, :, :][byte_mask], gt_sin)
      cos_val = self.angle_loss(angle_pred[:, 1, :, :][byte_mask], gt_cos)
      
      if not np.isnan(sin_val.data.cpu().numpy()): 
        self.angle_loss_value += sin_val
      if not np.isnan(cos_val.data.cpu().numpy()):
        self.angle_loss_value += cos_val
      
      iou_loss(roi_gt, byte_mask, roi_pred[0], self.box_loss_value)
        
      if self.multi_scale:
        byte_mask = torch.gt(F.interpolate(masked_segm.unsqueeze(1), size=(segm_pred1.size(1), segm_pred1.size(2)), mode='bilinear', align_corners=True), 0.5).squeeze(1)
        if byte_mask.sum() > 0:
          
          angle_gts = F.interpolate(angle_gt.unsqueeze(1), size=(segm_pred1.size(1), segm_pred1.size(2)), mode='bilinear', align_corners=True).squeeze(1)
          gt_sin = torch.sin(angle_gts[byte_mask])
          gt_cos = torch.cos(angle_gts[byte_mask])
          sin_val = self.angle_loss(angle_preds[1][:, 0, :, :][byte_mask], gt_sin)
          
          if not np.isnan(sin_val.data.cpu().numpy()): 
            self.angle_loss_value += sin_val
          
          cos_val = self.angle_loss(angle_preds[1][:, 1, :, :][byte_mask], gt_cos)  
          if not np.isnan(cos_val.data.cpu().numpy()): 
            self.angle_loss_value += cos_val
          
          roi_gt_s = F.interpolate(roi_gt.permute(0, 3, 1, 2), size=(segm_pred1.size(1), segm_pred1.size(2)), mode='bilinear', align_corners=True) / 2
          roi_gt_s = roi_gt_s.permute(0, 2, 3, 1)
          roi_gt_s = roi_gt_s / 2
          iou_loss(roi_gt_s, byte_mask, roi_pred[1], self.box_loss_value)
    
    return torch.stack( (self.iou_loss_value, self.angle_loss_value, self.box_loss_value) )