Mccn trainer

• Adds num_worker option to trainer
• Adds a logger to save losses, images, and gradients which can be visualized with tensorboard
• Adds a new architecture of mccnn with shared layers, MCCNNv2 and its extractor
• Added a RandomHorizontalFlipImage class for Multi channel images which works with numpy.ndarrays (solves the requirement to transform to PIL image for using the torchvision version, which doesnt work for num_channels>4)

bob/learn/pytorch/architectures/MCCNNv2.py

+#!/usr/bin/env python
+# encoding: utf-8
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import os
+import numpy as np
+
+import pkg_resources 
+import bob.extension.download
+
+import bob.io.base
+
+from .utils import MaxFeatureMap
+from .utils import group
+from .utils import resblock
+
+import logging
+logger = logging.getLogger("bob.learn.pytorch")
+
+class MCCNNv2(nn.Module):
+  """ The class defining the MCCNNv2 the difference from MCCNN is that it uses shared layers for
+  layers which are not adapted. This avoids replicating shared layers.
+  
+  Attributes
+  ----------
+  num_channels: int
+    The number of channels present in the input
+  lcnn_layers: list
+  	The adaptable layers present in the base LightCNN model
+  module_dict: dict
+  	A dictionary containing module names and `torch.nn.Module` elements as key, value pairs.
+  layer_dict: :py:class:`torch.nn.ModuleDict`
+  	Pytorch class containing the modules as a dictionary. 
+  light_cnn_model_file: str
+  	Absolute path to the pretrained LightCNN model file. 
+  adapted_layers: str
+    The layers to be adapted in training, they are to be separated by '-'. 
+    Example: 'conv1-block1-group1-ffc'; 'ffc' denotes final fully connected layers which
+    are adapted in all the cases. 
+  url: str
+  	The path to download the pretrained LightCNN model from.
+  
+  """
+  def __init__(self, block=resblock, layers=[1, 2, 3, 4], num_channels=4, adapted_layers = 'conv1-block1-group1-ffc', verbosity_level=2):
+    """ Init function
+
+    Parameters
+    ----------
+
+    num_channels: int
+      The number of channels present in the input
+    adapted_layers: str
+      The layers to be adapted in training, they are to be separated by '-'. 
+      Example: 'conv1-block1-group1-ffc'; 'ffc' denotes final fully connected layers which
+      are adapted in all the cases.
+    verbosity_level: int
+      Verbosity level.
+    
+    """
+    super(MCCNNv2, self).__init__()
+
+    self.num_channels=num_channels
+
+    self.lcnn_layers=['conv1','block1','group1','block2', 'group2','block3','group3','block4','group4','fc']
+
+    layers_present = self.lcnn_layers.copy()
+
+    layers_present.append('ffc')
+
+    # select the layers in the network to adapt
+
+    adapted_layers_list=adapted_layers.split('-')
+
+    assert('ffc' in adapted_layers_list)
+
+    assert(set(adapted_layers_list)<=set(layers_present)) # to ensure layer names are valid
+
+    self.shared_layers = list(set(layers_present) - set(adapted_layers_list)) # shared layers
+
+    self.domain_specific_layers= list(set(adapted_layers_list)-set(['ffc']))
+
+    logger.setLevel(verbosity_level)
+
+    self.pool1  = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
+    self.pool2  = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
+    self.pool3  = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
+    self.pool4  = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
+
+    # newly added FC layers
+
+    self.linear1fc=nn.Linear(256*num_channels,10)
+    self.linear2fc=nn.Linear(10,1)
+
+    # add modules 
+
+    module_dict={}
+
+    for i in range(self.num_channels):
+
+      m_dict={}
+
+      m_dict['conv1']  = MaxFeatureMap(1, 48, 5, 1, 2)
+      m_dict['block1'] = self._make_layer(block, layers[0], 48, 48)
+      m_dict['group1'] = group(48, 96, 3, 1, 1)
+      m_dict['block2'] = self._make_layer(block, layers[1], 96, 96)
+      m_dict['group2'] = group(96, 192, 3, 1, 1)
+      m_dict['block3'] = self._make_layer(block, layers[2], 192, 192)
+      m_dict['group3'] = group(192, 128, 3, 1, 1)
+      m_dict['block4'] = self._make_layer(block, layers[3], 128, 128)
+      m_dict['group4'] = group(128, 128, 3, 1, 1)
+      m_dict['fc']   = MaxFeatureMap(8*8*128, 256, type=0)
+
+      # ch_0_should be the anchor 
+
+      for layer in self.domain_specific_layers: # needs copies for domain specific layers
+
+        layer_name="ch_{}_".format(i)+layer
+
+        module_dict[layer_name] = m_dict[layer]
+
+    m_dict={}
+
+    m_dict['conv1']  = MaxFeatureMap(1, 48, 5, 1, 2)
+    m_dict['block1'] = self._make_layer(block, layers[0], 48, 48)
+    m_dict['group1'] = group(48, 96, 3, 1, 1)
+    m_dict['block2'] = self._make_layer(block, layers[1], 96, 96)
+    m_dict['group2'] = group(96, 192, 3, 1, 1)
+    m_dict['block3'] = self._make_layer(block, layers[2], 192, 192)
+    m_dict['group3'] = group(192, 128, 3, 1, 1)
+    m_dict['block4'] = self._make_layer(block, layers[3], 128, 128)
+    m_dict['group4'] = group(128, 128, 3, 1, 1)
+    m_dict['fc']   = MaxFeatureMap(8*8*128, 256, type=0)
+
+    for layer in self.shared_layers: # shared layers have ch_0_ prefix to make loading from pretrained model easier.
+
+        layer_name="ch_0_"+layer
+
+        module_dict[layer_name] = m_dict[layer]
+
+    self.layer_dict = nn.ModuleDict(module_dict)
+
+    # check for pretrained model 
+
+    light_cnn_model_file = os.path.join(MCCNNv2.get_mccnnv2path(), "LightCNN_29Layers_checkpoint.pth.tar")
+
+    url='http://www.idiap.ch/software/bob/data/bob/bob.learn.pytorch/master/LightCNN_29Layers_checkpoint.pth.tar'
+
+    logger.info("Light_cnn_model_file path: {}".format(light_cnn_model_file))
+
+
+    if not os.path.exists(light_cnn_model_file):
+
+      bob.io.base.create_directories_safe(os.path.split(light_cnn_model_file)[0])
+
+      logger.info('Downloading the LightCNN model')
+
+      bob.extension.download.download_file(url,light_cnn_model_file)
+
+      logger.info('Downloaded LightCNN model to location: {}'.format(light_cnn_model_file))
+
+
+    ## Loding the pretrained model for ch_0
+
+    self.load_state_dict(self.get_model_state_dict(light_cnn_model_file),strict=False)
+
+    # copy over the weights to all other domain specific layers
+
+    for layer in self.domain_specific_layers:
+
+      for i in range(1, self.num_channels): # except for 0 th channel
+
+        self.layer_dict["ch_{}_".format(i)+layer].load_state_dict(self.layer_dict["ch_0_"+layer].state_dict())  
+
+            
+  def _make_layer(self, block, num_blocks, in_channels, out_channels):
+    """ makes multiple copies of the same base module
+
+    Parameters
+    ----------
+    block: :py:class:`torch.nn.Module`
+      The base block to replicate
+    num_blocks: int
+      Number of copies of the block to be made
+    in_channels: int
+      Number of input channels for a block
+    out_channels: int
+      Number of output channels for a block
+    """
+    layers = []
+    for i in range(0, num_blocks):
+        layers.append(block(in_channels, out_channels))
+    return nn.Sequential(*layers)
+
+  def forward(self, img):
+    """ Propagate data through the network
+
+    Parameters
+    ----------
+    img: :py:class:`torch.Tensor` 
+      The data to forward through the network. Image of size num_channelsx128x128
+
+    Returns
+    -------
+    output: :py:class:`torch.Tensor` 
+      score 
+
+    """
+    
+    embeddings=[]
+
+    for i in range(self.num_channels):
+
+      commom_layer = lambda x,y: x if self.lcnn_layers[y] in self.domain_specific_layers else 0
+
+      # for ll in range(0,10):
+      #   logger.debug("ch_{}_".format(commom_layer(i,ll))+self.lcnn_layers[ll])
+
+      x=img[:,i,:,:].unsqueeze(1) # the image for the specific channel
+
+      x = self.layer_dict["ch_{}_".format(commom_layer(i,0))+self.lcnn_layers[0]](x)
+      x = self.pool1(x)
+
+      x = self.layer_dict["ch_{}_".format(commom_layer(i,1))+self.lcnn_layers[1]](x)
+      x = self.layer_dict["ch_{}_".format(commom_layer(i,2))+self.lcnn_layers[2]](x)
+      x = self.pool2(x)
+
+      x = self.layer_dict["ch_{}_".format(commom_layer(i,3))+self.lcnn_layers[3]](x)
+      x = self.layer_dict["ch_{}_".format(commom_layer(i,4))+self.lcnn_layers[4]](x)
+      x = self.pool3(x)
+
+      x = self.layer_dict["ch_{}_".format(commom_layer(i,5))+self.lcnn_layers[5]](x)
+      x = self.layer_dict["ch_{}_".format(commom_layer(i,6))+self.lcnn_layers[6]](x)
+      x = self.layer_dict["ch_{}_".format(commom_layer(i,7))+self.lcnn_layers[7]](x)
+      x = self.layer_dict["ch_{}_".format(commom_layer(i,8))+self.lcnn_layers[8]](x)
+      x = self.pool4(x)
+
+      x = x.view(x.size(0), -1)
+
+      fc = self.layer_dict["ch_{}_".format(commom_layer(i,9))+self.lcnn_layers[9]](x)
+
+      fc = F.dropout(fc, training=self.training)
+
+      embeddings.append(fc)
+
+    merged = torch.cat(embeddings, 1)
+
+    output = self.linear1fc(merged)
+
+    output = nn.Sigmoid()(output)
+
+    output = self.linear2fc(output)
+
+    if self.training:
+
+      output=nn.Sigmoid()(output)
+
+    return output
+
+  @staticmethod
+  def get_mccnnv2path():
+
+    import pkg_resources
+    return pkg_resources.resource_filename('bob.learn.pytorch', 'models')
+
+
+  def get_model_state_dict(self,pretrained_model_path):
+
+
+    """ The class to load pretrained LightCNN model
+
+    Attributes
+    ----------
+    pretrained_model_path: str
+      Absolute path to the LightCNN model file
+
+    new_state_dict: dict
+      Dictionary with LightCNN weights
+
+    """
+
+    checkpoint = torch.load(pretrained_model_path,map_location=lambda storage,loc:storage)
+    start_epoch = checkpoint['epoch']
+    state_dict = checkpoint['state_dict']
+    # create new OrderedDict that does not contain `module.`
+    from collections import OrderedDict
+    new_state_dict = OrderedDict()
+    for k, v in state_dict.items():
+      name = 'layer_dict.ch_0_'+k[7:] # remove `module.`
+      new_state_dict[name] = v
+    # load params
+    return new_state_dict