Source code for nolearn.decaf

import os
import sys

from nolearn import cache
import numpy as np
from sklearn.base import BaseEstimator

def _transform_cache_key(self, X):
    if len(X) == 1:
        raise cache.DontCache
    return ','.join([

[docs]class ConvNetFeatures(BaseEstimator): """Extract features from images using a pretrained ConvNet. Based on Yangqing Jia and Jeff Donahue's `DeCAF <>`_. Please make sure you read and accept DeCAF's license before you use this class. If ``classify_direct=False``, expects its input X to be a list of image filenames or arrays as produced by `np.array(`. """ verbose = 0
[docs] def __init__( self, feature_layer='fc7_cudanet_out', pretrained_params='imagenet.decafnet.epoch90', pretrained_meta='imagenet.decafnet.meta', center_only=True, classify_direct=False, verbose=0, ): """ :param feature_layer: The ConvNet layer that's used for feature extraction. Defaults to `fc7_cudanet_out`. A description of all available layers for the ImageNet-1k-pretrained ConvNet is found in the DeCAF wiki. They are: - `pool5_cudanet_out` - `fc6_cudanet_out` - `fc6_neuron_cudanet_out` - `fc7_cudanet_out` - `fc7_neuron_cudanet_out` - `probs_cudanet_out` :param pretrained_params: This must point to the file with the pretrained parameters. Defaults to `imagenet.decafnet.epoch90`. For the ImageNet-1k-pretrained ConvNet this file can be obtained from here: :param pretrained_meta: Similar to `pretrained_params`, this must file to the file with the pretrained parameters' metadata. Defaults to `imagenet.decafnet.meta`. :param center_only: Use the center patch of the image only when extracting features. If `False`, use four corners, the image center and flipped variants and average a total of 10 feature vectors, which will usually yield better results. Defaults to `True`. :param classify_direct: When `True`, assume that input X is an array of shape (num x 256 x 256 x 3) as returned by `prepare_image`. """ self.feature_layer = feature_layer self.pretrained_params = pretrained_params self.pretrained_meta = pretrained_meta self.center_only = center_only self.classify_direct = classify_direct self.net_ = None if (not os.path.exists(pretrained_params) or not os.path.exists(pretrained_meta)): raise ValueError( "Pre-trained ConvNet parameters not found. You may" "need to download the files from " " and " "pass the path to the two files as `pretrained_params` and " "`pretrained_meta` to the `{}` estimator.".format( self.__class__.__name__))
def fit(self, X=None, y=None): from decaf.scripts.imagenet import DecafNet # soft dep if self.net_ is None: self.net_ = DecafNet( self.pretrained_params, self.pretrained_meta, ) return self @cache.cached(_transform_cache_key) def transform(self, X): features = [] for img in X: if self.classify_direct: images = self.net_.oversample( img, center_only=self.center_only) self.net_.classify_direct(images) else: if isinstance(img, str): import Image # soft dep img = np.array( self.net_.classify(img, center_only=self.center_only) feat = None for layer in self.feature_layer.split(','): val = self.net_.feature(layer) if feat is None: feat = val else: feat = np.hstack([feat, val]) if not self.center_only: feat = feat.flatten() features.append(feat) if self.verbose: sys.stdout.write( "\r[ConvNet] %d%%" % (100. * len(features) / len(X))) sys.stdout.flush() if self.verbose: sys.stdout.write('\n') return np.vstack(features)
[docs] def prepare_image(self, image): """Returns image of shape `(256, 256, 3)`, as expected by `transform` when `classify_direct = True`. """ from decaf.util import transform # soft dep _JEFFNET_FLIP = True # first, extract the 256x256 center. image = transform.scale_and_extract(transform.as_rgb(image), 256) # convert to [0,255] float32 image = image.astype(np.float32) * 255. if _JEFFNET_FLIP: # Flip the image if necessary, maintaining the c_contiguous order image = image[::-1, :].copy() # subtract the mean image -= self.net_._data_mean return image