"""
FCN16 class.

Library:	Tensowflow 2.2.0, pyTorch 1.5.1
Author:		<NAME>
Email:		<EMAIL>
"""
from __future__ import absolute_import, division, print_function

import torch
from ..encoders.squeeze_extractor import *

class FCN16(torch.nn.Module):

	def __init__(self, n_classes, pretrained_model: SqueezeExtractor):
		super(FCN16, self).__init__()
		self.pretrained_model = pretrained_model
		self.features = self.pretrained_model.features
		self.copy_feature_info = pretrained_model.get_copy_feature_info()

		self.score_pool4 = nn.Conv2d(self.copy_feature_info[-2].out_channels,
									 n_classes, kernel_size=1)

		self.upsampling2 = nn.ConvTranspose2d(n_classes, n_classes, kernel_size=4,
											  stride=2, bias=False)
		self.upsampling16 = nn.ConvTranspose2d(n_classes, n_classes, kernel_size=32,
											   stride=16, bias=False)

		for m in self.features.modules():
			if isinstance(m, nn.Conv2d):
				channels = m.out_channels

		self.classifier = nn.Sequential(nn.Conv2d(channels, n_classes, kernel_size=1), nn.Sigmoid())
		self._initialize_weights()

	def _initialize_weights(self):
		for m in self.modules():
			if isinstance(m, nn.Conv2d):
				nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
				if m.bias is not None:
					nn.init.constant_(m.bias, 0)
			elif isinstance(m, nn.Linear):
				nn.init.normal_(m.weight, 0, 0.01)
				nn.init.constant_(m.bias, 0)

	def forward(self, x):
		last_feature_index = self.copy_feature_info[-2].index

		o = x
		for i in range(len(self.features)):
			o = self.features[i](o)
			if i == last_feature_index:
				pool4 = o

		o = self.classifier(o)
		o = self.upsampling2(o)

		o2 = self.score_pool4(pool4)
		o = o[:, :, 1:1 + o2.size()[2], 1:1 + o2.size()[3]]
		o = o + o2

		o = self.upsampling16(o)
		cx = int((o.shape[3] - x.shape[3]) / 2)
		cy = int((o.shape[2] - x.shape[2]) / 2)
		o = o[:, :, cy:cy + x.shape[2], cx:cx + x.shape[3]]

		return o

from ..encoders.vgg import *
from ..encoders.resnet import *
from ..encoders.mobilenet import *

def fcn16_vgg11(n_classes, batch_size, pretrained=False, fixed_feature=True):
	batch_norm = False if batch_size == 1 else True
	vgg = vgg_11(batch_norm, pretrained, fixed_feature)
	return FCN16(n_classes, vgg)
def fcn16_vgg13(n_classes, batch_size, pretrained=False, fixed_feature=True):
	batch_norm = False if batch_size == 1 else True
	vgg = vgg_13(batch_norm, pretrained, fixed_feature)
	return FCN16(n_classes, vgg)
def fcn16_vgg16(n_classes, batch_size, pretrained=False, fixed_feature=True):
	batch_norm = False if batch_size == 1 else True
	vgg = vgg_16(batch_norm, pretrained, fixed_feature)
	return FCN16(n_classes, vgg)
def fcn16_vgg19(n_classes, batch_size, pretrained=False, fixed_feature=True):
	batch_norm = False if batch_size == 1 else True
	vgg = vgg_19(batch_norm, pretrained, fixed_feature)
	return FCN16(n_classes, vgg)

def fcn16_resnet18(n_classes, batch_size, pretrained=False, fixed_feature=True):
	batch_norm = False if batch_size == 1 else True
	resnet = resnet18(pretrained, fixed_feature)
	return FCN16(n_classes, resnet)
def fcn16_resnet34(n_classes, batch_size, pretrained=False, fixed_feature=True):
	batch_norm = False if batch_size == 1 else True
	resnet = resnet34(pretrained, fixed_feature)
	return FCN16(n_classes, resnet)
def fcn16_resnet50(n_classes, batch_size, pretrained=False, fixed_feature=True):
	batch_norm = False if batch_size == 1 else True
	resnet = resnet50(pretrained, fixed_feature)
	return FCN16(n_classes, resnet)
def fcn16_resnet101(n_classes, batch_size, pretrained=False, fixed_feature=True):
	batch_norm = False if batch_size == 1 else True
	resnet = resnet101(pretrained, fixed_feature)
	return FCN16(n_classes, resnet)
def fcn16_resnet152(n_classes, batch_size, pretrained=False, fixed_feature=True):
	batch_norm = False if batch_size == 1 else True
	resnet = resnet152(pretrained, fixed_feature)
	return FCN16(n_classes, resnet)

def fcn16_mobilenet_v2(n_classes, batch_size, pretrained=False, fixed_feature=True):
	batch_norm = False if batch_size == 1 else True
	mobile_net = mobilenet(pretrained, fixed_feature)
	return FCN16(n_classes, mobile_net)
"""
FCN16 class.

Library:	Tensowflow 2.2.0, pyTorch 1.5.1
Author:		<NAME>
Email:		<EMAIL>
"""
from __future__ import absolute_import, division, print_function

import torch
from ..encoders.squeeze_extractor import *

class FCN16(torch.nn.Module):

	def __init__(self, n_classes, pretrained_model: SqueezeExtractor):
		super(FCN16, self).__init__()
		self.pretrained_model = pretrained_model
		self.features = self.pretrained_model.features
		self.copy_feature_info = pretrained_model.get_copy_feature_info()

		self.score_pool4 = nn.Conv2d(self.copy_feature_info[-2].out_channels,
									 n_classes, kernel_size=1)

		self.upsampling2 = nn.ConvTranspose2d(n_classes, n_classes, kernel_size=4,
											  stride=2, bias=False)
		self.upsampling16 = nn.ConvTranspose2d(n_classes, n_classes, kernel_size=32,
											   stride=16, bias=False)

		for m in self.features.modules():
			if isinstance(m, nn.Conv2d):
				channels = m.out_channels

		self.classifier = nn.Sequential(nn.Conv2d(channels, n_classes, kernel_size=1), nn.Sigmoid())
		self._initialize_weights()

	def _initialize_weights(self):
		for m in self.modules():
			if isinstance(m, nn.Conv2d):
				nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
				if m.bias is not None:
					nn.init.constant_(m.bias, 0)
			elif isinstance(m, nn.Linear):
				nn.init.normal_(m.weight, 0, 0.01)
				nn.init.constant_(m.bias, 0)

	def forward(self, x):
		last_feature_index = self.copy_feature_info[-2].index

		o = x
		for i in range(len(self.features)):
			o = self.features[i](o)
			if i == last_feature_index:
				pool4 = o

		o = self.classifier(o)
		o = self.upsampling2(o)

		o2 = self.score_pool4(pool4)
		o = o[:, :, 1:1 + o2.size()[2], 1:1 + o2.size()[3]]
		o = o + o2

		o = self.upsampling16(o)
		cx = int((o.shape[3] - x.shape[3]) / 2)
		cy = int((o.shape[2] - x.shape[2]) / 2)
		o = o[:, :, cy:cy + x.shape[2], cx:cx + x.shape[3]]

		return o

from ..encoders.vgg import *
from ..encoders.resnet import *
from ..encoders.mobilenet import *

def fcn16_vgg11(n_classes, batch_size, pretrained=False, fixed_feature=True):
	batch_norm = False if batch_size == 1 else True
	vgg = vgg_11(batch_norm, pretrained, fixed_feature)
	return FCN16(n_classes, vgg)
def fcn16_vgg13(n_classes, batch_size, pretrained=False, fixed_feature=True):
	batch_norm = False if batch_size == 1 else True
	vgg = vgg_13(batch_norm, pretrained, fixed_feature)
	return FCN16(n_classes, vgg)
def fcn16_vgg16(n_classes, batch_size, pretrained=False, fixed_feature=True):
	batch_norm = False if batch_size == 1 else True
	vgg = vgg_16(batch_norm, pretrained, fixed_feature)
	return FCN16(n_classes, vgg)
def fcn16_vgg19(n_classes, batch_size, pretrained=False, fixed_feature=True):
	batch_norm = False if batch_size == 1 else True
	vgg = vgg_19(batch_norm, pretrained, fixed_feature)
	return FCN16(n_classes, vgg)

def fcn16_resnet18(n_classes, batch_size, pretrained=False, fixed_feature=True):
	batch_norm = False if batch_size == 1 else True
	resnet = resnet18(pretrained, fixed_feature)
	return FCN16(n_classes, resnet)
def fcn16_resnet34(n_classes, batch_size, pretrained=False, fixed_feature=True):
	batch_norm = False if batch_size == 1 else True
	resnet = resnet34(pretrained, fixed_feature)
	return FCN16(n_classes, resnet)
def fcn16_resnet50(n_classes, batch_size, pretrained=False, fixed_feature=True):
	batch_norm = False if batch_size == 1 else True
	resnet = resnet50(pretrained, fixed_feature)
	return FCN16(n_classes, resnet)
def fcn16_resnet101(n_classes, batch_size, pretrained=False, fixed_feature=True):
	batch_norm = False if batch_size == 1 else True
	resnet = resnet101(pretrained, fixed_feature)
	return FCN16(n_classes, resnet)
def fcn16_resnet152(n_classes, batch_size, pretrained=False, fixed_feature=True):
	batch_norm = False if batch_size == 1 else True
	resnet = resnet152(pretrained, fixed_feature)
	return FCN16(n_classes, resnet)

def fcn16_mobilenet_v2(n_classes, batch_size, pretrained=False, fixed_feature=True):
	batch_norm = False if batch_size == 1 else True
	mobile_net = mobilenet(pretrained, fixed_feature)
	return FCN16(n_classes, mobile_net)
FCN16 class.  Library:	Tensowflow 2.2.0, pyTorch 1.5.1 Author:		<NAME> Email:		<EMAIL>