shownet_codalab.py

import numpy
import sys
import getopt as opt
from util import *
from math import sqrt, ceil, floor
import os
from gpumodel import IGPUModel
import random as r
import numpy.random as nr
try:
    import pylab as pl
except:
    print "This script requires the matplotlib python library (Ubuntu/Fedora package name python-matplotlib). Please install it."
    sys.exit(1)

from Main_3DCNN import ConvNet

from options import *

class ShowNetError(Exception):
    pass

class ShowConvNet(ConvNet):
    def __init__(self, op, load_dic):
        ConvNet.__init__(self, op, load_dic)
    
    def get_gpus(self):
        self.need_gpu = self.op.get_value('show_preds') or self.op.get_value('write_features')
        if self.need_gpu:
            ConvNet.get_gpus(self)
    
    def init_data_providers(self):
        class Dummy:
            def advance_batch(self):
                pass
        if self.need_gpu:
            ConvNet.init_data_providers(self)
        else:
            self.train_data_provider = self.test_data_provider = Dummy()
    
    def import_model(self):
        if self.need_gpu:
            ConvNet.import_model(self)
            
    def init_model_state(self):
        #ConvNet.init_model_state(self)
        if self.op.get_value('show_preds'):
            self.sotmax_idx = self.get_layer_idx(self.op.get_value('show_preds'), check_type='softmax')
        if self.op.get_value('write_features'):
            self.ftr_layer_idx = self.get_layer_idx(self.op.get_value('write_features'))
            
    def init_model_lib(self):
        if self.need_gpu:
            ConvNet.init_model_lib(self)

    def plot_cost(self):
        if self.show_cost not in self.train_outputs[0][0]:
            raise ShowNetError("Cost function with name '%s' not defined by given convnet." % self.show_cost)
        train_errors = [o[0][self.show_cost][self.cost_idx] for o in self.train_outputs]
        test_errors = [o[0][self.show_cost][self.cost_idx] for o in self.test_outputs]

        numbatches = len(self.train_batch_range)
        test_errors = numpy.row_stack(test_errors)
        test_errors = numpy.tile(test_errors, (1, self.testing_freq))
        test_errors = list(test_errors.flatten())
        test_errors += [test_errors[-1]] * max(0,len(train_errors) - len(test_errors))
        test_errors = test_errors[:len(train_errors)]

        numepochs = len(train_errors) / float(numbatches)
        pl.figure(1)
        x = range(0, len(train_errors))
        pl.plot(x, train_errors, 'k-', label='Training set')
        pl.plot(x, test_errors, 'r-', label='Test set')
        pl.legend()
        ticklocs = range(numbatches, len(train_errors) - len(train_errors) % numbatches + 1, numbatches)
        epoch_label_gran = int(ceil(numepochs / 20.)) # aim for about 20 labels
        epoch_label_gran = int(ceil(float(epoch_label_gran) / 10) * 10) # but round to nearest 10
        ticklabels = map(lambda x: str((x[1] / numbatches)) if x[0] % epoch_label_gran == epoch_label_gran-1 else '', enumerate(ticklocs))

        pl.xticks(ticklocs, ticklabels)
        pl.xlabel('Epoch')
#        pl.ylabel(self.show_cost)
        pl.title(self.show_cost)
        
    def make_filter_fig(self, filters, filter_start, fignum, _title, num_filters, combine_chans):
        FILTERS_PER_ROW = 16
        MAX_ROWS = 16
        MAX_FILTERS = FILTERS_PER_ROW * MAX_ROWS
        num_colors = filters.shape[0]
        f_per_row = int(ceil(FILTERS_PER_ROW / float(1 if combine_chans else num_colors)))
        filter_end = min(filter_start+MAX_FILTERS, num_filters)
        filter_rows = int(ceil(float(filter_end - filter_start) / f_per_row))
    
        filter_size = int(sqrt(filters.shape[1]))
        fig = pl.figure(fignum)
        fig.text(.5, .95, '%s %dx%d filters %d-%d' % (_title, filter_size, filter_size, filter_start, filter_end-1), horizontalalignment='center') 
        num_filters = filter_end - filter_start
        if not combine_chans:
            bigpic = n.zeros((filter_size * filter_rows + filter_rows + 1, filter_size*num_colors * f_per_row + f_per_row + 1), dtype=n.single)
        else:
            bigpic = n.zeros((3, filter_size * filter_rows + filter_rows + 1, filter_size * f_per_row + f_per_row + 1), dtype=n.single)
    
        for m in xrange(filter_start,filter_end ):
            filter = filters[:,:,m]
            y, x = (m - filter_start) / f_per_row, (m - filter_start) % f_per_row
            if not combine_chans:
                for c in xrange(num_colors):
                    filter_pic = filter[c,:].reshape((filter_size,filter_size))
                    bigpic[1 + (1 + filter_size) * y:1 + (1 + filter_size) * y + filter_size,
                           1 + (1 + filter_size*num_colors) * x + filter_size*c:1 + (1 + filter_size*num_colors) * x + filter_size*(c+1)] = filter_pic
            else:
                filter_pic = filter.reshape((3, filter_size,filter_size))
                bigpic[:,
                       1 + (1 + filter_size) * y:1 + (1 + filter_size) * y + filter_size,
                       1 + (1 + filter_size) * x:1 + (1 + filter_size) * x + filter_size] = filter_pic
                
        pl.xticks([])
        pl.yticks([])
        if not combine_chans:
            pl.imshow(bigpic, cmap=pl.cm.gray, interpolation='nearest')
        else:
            bigpic = bigpic.swapaxes(0,2).swapaxes(0,1)
            pl.imshow(bigpic, interpolation='nearest')        
        
    def plot_filters(self):
        filter_start = 0 # First filter to show
        layer_names = [l['name'] for l in self.layers]
        if self.show_filters not in layer_names:
            raise ShowNetError("Layer with name '%s' not defined by given convnet." % self.show_filters)
        layer = self.layers[layer_names.index(self.show_filters)]
        filters = layer['weights'][self.input_idx]
        if layer['type'] == 'fc': # Fully-connected layer
            num_filters = layer['outputs']
            channels = self.channels
        elif layer['type'] in ('conv', 'local'): # Conv layer
            num_filters = layer['filters']
            channels = layer['filterChannels'][self.input_idx]
            if layer['type'] == 'local':
                filters = filters.reshape((layer['modules'], layer['filterPixels'][self.input_idx] * channels, num_filters))
                filters = filters.swapaxes(0,1).reshape(channels * layer['filterPixels'][self.input_idx], num_filters * layer['modules'])
                num_filters *= layer['modules']
            #if layer['type'] == 'local':
            #    filters = filters.reshape((channels*layer['filterPixels'][self.input_idx],layer['modules'], num_filters))
            #    filters = filters.reshape((channels*layer['filterPixels'][self.input_idx], num_filters*layer['modules']))
            #    #filters = filters.swapaxes(0,1).reshape(channels * layer['filterPixels'][self.input_idx], num_filters * layer['modules'])
            #    num_filters *= layer['modules']

        filters = filters.reshape(channels, filters.shape[0]/channels, filters.shape[1])
        #filters = filters.swapaxes(0,1)
        # Convert YUV filters to RGB
        if self.yuv_to_rgb and channels == 3:
            R = filters[0,:,:] + 1.28033 * filters[2,:,:]
            G = filters[0,:,:] + -0.21482 * filters[1,:,:] + -0.38059 * filters[2,:,:]
            B = filters[0,:,:] + 2.12798 * filters[1,:,:]
            filters[0,:,:], filters[1,:,:], filters[2,:,:] = R, G, B
        combine_chans = not self.no_rgb and channels == 3
        

        #filters = filters[:,:,3000:3064]
        #num_filters = 64
        # Make sure you don't modify the backing array itself here -- so no -= or /=
        filters = filters - filters.min()
        filters = filters / filters.max()

        #f_min = filters.min(axis=0)
        #f_min = f_min.min(axis=0)
        #filters = filters - f_min
        #f_max = filters.max(axis=0)
        #f_max = f_max.max(axis=0)
        #filters = filters / f_max

        self.make_filter_fig(filters, filter_start, 2, 'Layer %s' % self.show_filters, num_filters, combine_chans)
    
    def plot_predictions(self):
        data = self.get_next_batch(train=False)[2] # get a test batch
        num_classes = self.test_data_provider.get_num_classes()
        NUM_ROWS = 2
        NUM_COLS = 4
        NUM_IMGS = NUM_ROWS * NUM_COLS
        NUM_TOP_CLASSES = min(num_classes, 4) # show this many top labels
        
        label_names = self.test_data_provider.batch_meta['label_names']
        if self.only_errors:
            preds = n.zeros((data[0].shape[1], num_classes), dtype=n.single)
        else:
            preds = n.zeros((NUM_IMGS, num_classes), dtype=n.single)
            rand_idx = nr.randint(0, data[0].shape[1], NUM_IMGS)
            data[0] = n.require(data[0][:,rand_idx], requirements='C')
            data[1] = n.require(data[1][:,rand_idx], requirements='C')
        data += [preds]

        # Run the model
        self.libmodel.startFeatureWriter(data, self.sotmax_idx)
        self.finish_batch()
        
        fig = pl.figure(3)
        fig.text(.4, .95, '%s test case predictions' % ('Mistaken' if self.only_errors else 'Random'))
        if self.only_errors:
            err_idx = nr.permutation(n.where(preds.argmax(axis=1) != data[1][0,:])[0])[:NUM_IMGS] # what the net got wrong
            data[0], data[1], preds = data[0][:,err_idx], data[1][:,err_idx], preds[err_idx,:]
            
        data[0] = self.test_data_provider.get_plottable_data(data[0])
        for r in xrange(NUM_ROWS):
            for c in xrange(NUM_COLS):
                img_idx = r * NUM_COLS + c
                if data[0].shape[0] <= img_idx:
                    break
                pl.subplot(NUM_ROWS*2, NUM_COLS, r * 2 * NUM_COLS + c + 1)
                pl.xticks([])
                pl.yticks([])
                img = data[0][img_idx,:,:,:]
                pl.imshow(img, interpolation='nearest')
                true_label = int(data[1][0,img_idx])

                img_labels = sorted(zip(preds[img_idx,:], label_names), key=lambda x: x[0])[-NUM_TOP_CLASSES:]
                pl.subplot(NUM_ROWS*2, NUM_COLS, (r * 2 + 1) * NUM_COLS + c + 1, aspect='equal')

                ylocs = n.array(range(NUM_TOP_CLASSES)) + 0.5
                height = 0.5
                width = max(ylocs)
                pl.barh(ylocs, [l[0]*width for l in img_labels], height=height, \
                        color=['r' if l[1] == label_names[true_label] else 'b' for l in img_labels])
                pl.title(label_names[true_label])
                pl.yticks(ylocs + height/2, [l[1] for l in img_labels])
                pl.xticks([width/2.0, width], ['50%', ''])
                pl.ylim(0, ylocs[-1] + height*2)
    

    def do_write_features(self, data):
        self.ftr_layer_idx = self.get_layer_idx('probs')
        num_ftrs = self.layers[self.ftr_layer_idx]['outputs']
        ftrs = n.zeros((data.shape[1], num_ftrs), dtype=n.single)
        labels_temp = n.zeros((1,data.shape[1]), dtype=n.single)
        self.libmodel.startFeatureWriter([data, labels_temp]+ [ftrs], self.ftr_layer_idx)           
        self.finish_batch()
        return ftrs

    #def do_write_features(self):
    #    if not os.path.exists(self.feature_path):
    #        os.makedirs(self.feature_path)
    #    next_data = self.get_next_batch(train=False)
    #    b1 = next_data[1]
    #    num_ftrs = self.layers[self.ftr_layer_idx]['outputs']
    #    while True:
    #        batch = next_data[1]
    #        data = next_data[2]
    #        ftrs = n.zeros((data[0].shape[1], num_ftrs), dtype=n.single)
    #        self.libmodel.startFeatureWriter(data + [ftrs], self.ftr_layer_idx)
            
    #        # load the next batch while the current one is computing
    #        next_data = self.get_next_batch(train=False)
    #        self.finish_batch()
    #        path_out = os.path.join(self.feature_path, 'data_batch_%d' % batch)
    #        pickle(path_out, {'data': ftrs, 'labels': data[1]})
    #        print "Wrote feature file %s" % path_out
    #        if next_data[1] == b1:
    #            break
    #    pickle(os.path.join(self.feature_path, 'batches.meta'), {'source_model':self.load_file,
    #                                                             'num_vis':num_ftrs})
                
    def start(self):
        self.op.print_values()
        if self.show_cost:
            self.plot_cost()
        if self.show_filters:
            self.plot_filters()
        if self.show_preds:
            self.plot_predictions()
        if self.write_features:
            self.do_write_features()
        pl.show()
        sys.exit(0)
            
    @classmethod
    def get_options_parser(cls):
        op = ConvNet.get_options_parser()
        for option in list(op.options):
            if option not in ('gpu', 'load_file', 'train_batch_range', 'test_batch_range'):
                op.delete_option(option)
        op.add_option("show-cost", "show_cost", StringOptionParser, "Show specified objective function", default="")
        op.add_option("show-filters", "show_filters", StringOptionParser, "Show learned filters in specified layer", default="")
        op.add_option("input-idx", "input_idx", IntegerOptionParser, "Input index for layer given to --show-filters", default=0)
        op.add_option("cost-idx", "cost_idx", IntegerOptionParser, "Cost function return value index for --show-cost", default=0)
        op.add_option("no-rgb", "no_rgb", BooleanOptionParser, "Don't combine filter channels into RGB in layer given to --show-filters", default=False)
        op.add_option("yuv-to-rgb", "yuv_to_rgb", BooleanOptionParser, "Convert RGB filters to YUV in layer given to --show-filters", default=False)
        op.add_option("channels", "channels", IntegerOptionParser, "Number of channels in layer given to --show-filters (fully-connected layers only)", default=0)
        op.add_option("show-preds", "show_preds", StringOptionParser, "Show predictions made by given softmax on test set", default="")
        op.add_option("only-errors", "only_errors", BooleanOptionParser, "Show only mistaken predictions (to be used with --show-preds)", default=False, requires=['show_preds'])
        op.add_option("write-features", "write_features", StringOptionParser, "Write test data features from given layer", default="", requires=['feature-path'])
        op.add_option("feature-path", "feature_path", StringOptionParser, "Write test data features to this path (to be used with --write-features)", default="")
        
        op.options['load_file'].default = None
        return op
    
if __name__ == "__main__":
    try:
        op = ShowConvNet.get_options_parser()
        op, load_dic = IGPUModel.parse_options(op)
        model = ShowConvNet(op, load_dic)
        model.start()
    except (UnpickleError, ShowNetError, opt.GetoptError), e:
        print "----------------"
        print "Error:"
        print e