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#!/usr/bin/python |
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# -*- coding: utf-8 -*- |
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# Author: Axel Angel, copyright 2015, license GPLv3. |
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import sys |
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import caffe |
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import numpy as np |
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import lmdb |
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import argparse |
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from collections import defaultdict |
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def lmdb_reader(fpath): |
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import lmdb |
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lmdb_env = lmdb.open(fpath) |
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lmdb_txn = lmdb_env.begin() |
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lmdb_cursor = lmdb_txn.cursor() |
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for key, value in lmdb_cursor: |
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datum = caffe.proto.caffe_pb2.Datum() |
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datum.ParseFromString(value) |
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label = int(datum.label) |
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image = caffe.io.datum_to_array(datum).astype(np.uint8) |
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yield (key, flat_shape(image), label) |
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def leveldb_reader(fpath): |
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import leveldb |
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db = leveldb.LevelDB(fpath) |
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for key, value in db.RangeIter(): |
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datum = caffe.proto.caffe_pb2.Datum() |
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datum.ParseFromString(value) |
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label = int(datum.label) |
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image = caffe.io.datum_to_array(datum).astype(np.uint8) |
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yield (key, flat_shape(image), label) |
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def npz_reader(fpath): |
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npz = np.load(fpath) |
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xs = npz['arr_0'] |
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ls = npz['arr_1'] |
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for i, (x, l) in enumerate(np.array([ xs, ls ]).T): |
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yield (i, x, l) |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--proto', type=str, required=True) |
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parser.add_argument('--model', type=str, required=True) |
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group = parser.add_mutually_exclusive_group(required=True) |
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group.add_argument('--lmdb', type=str, default=None) |
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group.add_argument('--leveldb', type=str, default=None) |
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group.add_argument('--npz', type=str, default=None) |
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args = parser.parse_args() |
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count = 0 |
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correct = 0 |
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matrix = defaultdict(int) # (real,pred) -> int |
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labels_set = set() |
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net = caffe.Net(args.proto, args.model, caffe.TEST) |
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caffe.set_mode_cpu() |
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print "args", vars(args) |
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if args.lmdb != None: |
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reader = lmdb_reader(args.lmdb) |
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if args.leveldb != None: |
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reader = leveldb_reader(args.leveldb) |
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if args.npz != None: |
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reader = npz_reader(args.npz) |
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for i, image, label in reader: |
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image_caffe = image.reshape(1, *image.shape) |
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out = net.forward_all(data=np.asarray([ image_caffe ])) |
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plabel = int(out['prob'][0].argmax(axis=0)) |
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count += 1 |
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iscorrect = label == plabel |
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correct += (1 if iscorrect else 0) |
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matrix[(label, plabel)] += 1 |
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labels_set.update([label, plabel]) |
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if not iscorrect: |
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print("\rError: i=%s, expected %i but predicted %i" \ |
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% (i, label, plabel)) |
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sys.stdout.write("\rAccuracy: %.1f%%" % (100.*correct/count)) |
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sys.stdout.flush() |
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print(", %i/%i corrects" % (correct, count)) |
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print "" |
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print "Confusion matrix:" |
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print "(r , p) | count" |
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for l in labels_set: |
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for pl in labels_set: |
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print "(%i , %i) | %i" % (l, pl, matrix[(l,pl)]) |
axel-angel revised this gist