# coding: utf-8

import torch.nn as nn

	
class C3D(nn.Module):
	"""
	nb_classes: nb_classes in classification task, 101 for UCF101 dataset
	"""

	def __init__(self, nb_classes):
		super(C3D, self).__init__()

		self.conv1 = nn.Conv3d(3, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1))
		self.pool1 = nn.MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2))

		self.conv2 = nn.Conv3d(64, 128, kernel_size=(3, 3, 3), padding=(1, 1, 1))
		self.pool2 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))

		self.conv3a = nn.Conv3d(128, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1))
		self.conv3b = nn.Conv3d(256, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1))
		self.pool3 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))

		self.conv4a = nn.Conv3d(256, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))
		self.conv4b = nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))
		self.pool4 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))

		self.conv5a = nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))
		self.conv5b = nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))
		self.pool5 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2), padding=(0, 1, 1))

		self.fc6 = nn.Linear(8192, 4096)
		self.fc7 = nn.Linear(4096, 4096)
		self.fc8 = nn.Linear(4096, nb_classes)

		self.dropout = nn.Dropout(p=0.5)

		self.relu = nn.ReLU()

	def forward(self, x, feature_layer):
		
		h = self.relu(self.conv1(x))
		h = self.pool1(h)
		h = self.relu(self.conv2(h))
		h = self.pool2(h)

		h = self.relu(self.conv3a(h))
		h = self.relu(self.conv3b(h))
		h = self.pool3(h)

		h = self.relu(self.conv4a(h))
		h = self.relu(self.conv4b(h))
		h = self.pool4(h)

		h = self.relu(self.conv5a(h))
		h = self.relu(self.conv5b(h))
		h = self.pool5(h)

		h = h.reshape(-1, 8192)
		out = h if feature_layer == 5 else None
		h = self.relu(self.fc6(h))
		out = h if feature_layer == 6 and out == None else out
		h = self.dropout(h)
		h = self.relu(self.fc7(h))
		out = h if feature_layer == 7 and out == None else out
		h = self.dropout(h)
		logits = self.fc8(h)
		return logits, out