def main():
# parse the command line arguments
parser = NeonArgparser(__doc__)
parser.add_argument('--output_path', required=True,
help='Output path used when training model')
parser.add_argument('--w2v_path', required=False, default=None,
help='Path to GoogleNews w2v file for voab expansion.')
parser.add_argument('--eval_data_path', required=False, default='./SICK_data',
help='Path to the SICK dataset for evaluating semantic relateness')
parser.add_argument('--max_vocab_size', required=False, default=1000000,
help='Limit the vocabulary expansion to fit in GPU memory')
parser.add_argument('--subset_pct', required=False, default=100,
help='subset of training dataset to use (use to retreive \
preprocessed data from training)')
args = parser.parse_args(gen_be=True)
# load vocab file from training
_, vocab_file = load_data(args.data_dir, output_path=args.output_path,
subset_pct=float(args.subset_pct))
vocab, _, _ = load_obj(vocab_file)
vocab_size = len(vocab)
neon_logger.display("\nVocab size from the dataset is: {}".format(vocab_size))
index_from = 2 # 0: padding 1: oov
vocab_size_layer = vocab_size + index_from
max_len = 30
# load trained model
model_dict = load_obj(args.model_file)
# Vocabulary expansion trick needs to pass the correct vocab set to evaluate (for tokenization)
if args.w2v_path:
neon_logger.display("Performing Vocabulary Expansion... Loading W2V...")
w2v_vocab, w2v_vocab_size = get_w2v_vocab(args.w2v_path,
int(args.max_vocab_size), cache=True)
vocab_size_layer = w2v_vocab_size + index_from
model = load_sent_encoder(model_dict, expand_vocab=True, orig_vocab=vocab,
w2v_vocab=w2v_vocab, w2v_path=args.w2v_path, use_recur_last=True)
vocab = w2v_vocab
else:
# otherwise stick with original vocab size used to train the model
model = load_sent_encoder(model_dict, use_recur_last=True)
model.initialize(dataset=(max_len, 1))
evaluate(model, vocab=vocab, data_path=args.eval_data_path, evaltest=True,
vocab_size_layer=vocab_size_layer)
def main():
# parse the command line arguments
parser = NeonArgparser(__doc__)
parser.add_argument('--output_path', required=True,
help='Output path used when training model')
parser.add_argument('--w2v_path', required=False, default=None,
help='Path to GoogleNews w2v file for voab expansion.')
parser.add_argument('--eval_data_path', required=False, default='./SICK_data',
help='Path to the SICK dataset for evaluating semantic relateness')
parser.add_argument('--max_vocab_size', required=False, default=1000000,
help='Limit the vocabulary expansion to fit in GPU memory')
parser.add_argument('--subset_pct', required=False, default=100,
help='subset of training dataset to use (use to retreive \
preprocessed data from training)')
args = parser.parse_args(gen_be=True)
# load vocab file from training
_, vocab_file = load_data(args.data_dir, output_path=args.output_path,
subset_pct=float(args.subset_pct))
vocab, _, _ = load_obj(vocab_file)
vocab_size = len(vocab)
neon_logger.display("\nVocab size from the dataset is: {}".format(vocab_size))
index_from = 2 # 0: padding 1: oov
vocab_size_layer = vocab_size + index_from
max_len = 30
# load trained model
model_dict = load_obj(args.model_file)
# Vocabulary expansion trick needs to pass the correct vocab set to evaluate (for tokenization)
if args.w2v_path:
neon_logger.display("Performing Vocabulary Expansion... Loading W2V...")
w2v_vocab, w2v_vocab_size = get_w2v_vocab(args.w2v_path,
int(args.max_vocab_size), cache=True)
vocab_size_layer = w2v_vocab_size + index_from
model = load_sent_encoder(model_dict, expand_vocab=True, orig_vocab=vocab,
w2v_vocab=w2v_vocab, w2v_path=args.w2v_path, use_recur_last=True)
vocab = w2v_vocab
else:
# otherwise stick with original vocab size used to train the model
model = load_sent_encoder(model_dict, use_recur_last=True)
model.initialize(dataset=(max_len, 1))
evaluate(model, vocab=vocab, data_path=args.eval_data_path, evaltest=True,
vocab_size_layer=vocab_size_layer)
def load_vgg_weights(model, path):
url = 'https://s3-us-west-1.amazonaws.com/nervana-modelzoo/'
filename = 'VGG_ILSVRC_16_layers_fc_reduced_fused_conv_bias.p'
size = 86046032
workdir, filepath = Dataset._valid_path_append(path, '', filename)
if not os.path.exists(filepath):
Dataset.fetch_dataset(url, filename, filepath, size)
print(
'De-serializing the pre-trained VGG16 model with dilated convolutions...'
)
pdict = load_obj(filepath)
model_layers = [l for l in model.layers.layers[0].layers]
# convert source model into dictionary with layer name as keys
src_layers = {
layer['config']['name']: layer
for layer in pdict['model']['config']['layers']
}
i = 0
for layer in model_layers:
if layer.classnm == 'Convolution_bias' and i < 15:
# no states in above parameter file
layer.load_weights(src_layers['Convolution_bias_' + str(i)],
load_states=False)
print('{} loaded from source file'.format(layer.name))
i += 1
elif hasattr(layer, 'W'):
print('Skipping {} layer'.format(layer.name))
def load_vgg_weights(model, path):
url = 'https://s3-us-west-1.amazonaws.com/nervana-modelzoo/'
filename = 'VGG_ILSVRC_16_layers_fc_reduced_fused_conv_bias.p'
size = 86046032
workdir, filepath = Dataset._valid_path_append(path, '', filename)
if not os.path.exists(filepath):
Dataset.fetch_dataset(url, filename, filepath, size)
print('De-serializing the pre-trained VGG16 model with dilated convolutions...')
pdict = load_obj(filepath)
model_layers = [l for l in model.layers.layers[0].layers]
# convert source model into dictionary with layer name as keys
src_layers = {layer['config']['name']: layer for layer in pdict['model']['config']['layers']}
i = 0
for layer in model_layers:
if layer.classnm == 'Convolution_bias' and i < 15:
# no states in above parameter file
layer.load_weights(src_layers['Convolution_bias_'+str(i)], load_states=False)
print('{} loaded from source file'.format(layer.name))
i += 1
elif hasattr(layer, 'W'):
print('Skipping {} layer'.format(layer.name))
def load_vgg_all_weights(model, path):
# load a pre-trained VGG16 from Neon model zoo to the local
url = 'https://s3-us-west-1.amazonaws.com/nervana-modelzoo/VGG/'
filename = 'VGG_D.p'
size = 554227541
workdir, filepath = Dataset._valid_path_append(path, '', filename)
if not os.path.exists(filepath):
Dataset.fetch_dataset(url, filename, filepath, size)
print('De-serializing the pre-trained VGG16 model...')
pdict = load_obj(filepath)
param_layers = [l for l in model.layers.layers[0].layers]
param_dict_list = pdict['model']['config']['layers']
i = 0
for layer, ps in zip(param_layers, param_dict_list):
i += 1
if i == 43:
break
layer.load_weights(ps, load_states=True)
print(layer.name + " <-- " + ps['config']['name'])
# to load the fc6 and fc7 from caffe into neon fc layers after ROI pooling
neon_fc_layers = model.layers.layers[2].layers[1].layers[0].layers[2:5] +\
model.layers.layers[2].layers[1].layers[0].layers[6:9]
vgg_fc_layers = param_dict_list[44:47] + param_dict_list[48:51]
for layer, ps in zip(neon_fc_layers, vgg_fc_layers):
layer.load_weights(ps, load_states=True)
print(layer.name + " <-- " + ps['config']['name'])
def load_imagenet_weights(model, path):
# load a pre-trained Alexnet from Neon model zoo to the local
url = 'https://s3-us-west-1.amazonaws.com/nervana-modelzoo/'
filename = 'alexnet.p'
size = 488808400
workdir, filepath = Dataset._valid_path_append(path, '', filename)
if not os.path.exists(filepath):
Dataset.fetch_dataset(url, filename, filepath, size)
print 'De-serializing the pre-trained Alexnet using ImageNet I1K ...'
pdict = load_obj(filepath)
param_layers = [l for l in model.layers_to_optimize]
param_dict_list = pdict['layer_params_states']
i = 0
for layer, ps in zip(param_layers, param_dict_list):
i = i+1
print i, layer.name
layer.set_params(ps)
if 'states' in ps:
layer.set_states(ps)
if i == 10:
print 'Only load the pre-trained weights up to conv5 layer of Alexnet'
break
def __init__(
self, repo_dir, inner_size, do_transforms=True, rgb=True, multiview=False, set_name="train", subset_pct=100
):
assert subset_pct > 0 and subset_pct <= 100, "subset_pct must be between 0 and 100"
assert set_name in ["train", "validation"]
self.set_name = set_name if set_name == "train" else "val"
self.repo_dir = repo_dir
self.inner_size = inner_size
self.minibatch_size = self.be.bsz
# Load from repo dataset_cache:
try:
cache_filepath = os.path.join(repo_dir, "dataset_cache.pkl")
dataset_cache = load_obj(cache_filepath)
except:
raise IOError(
"Cannot find dataset cache in %s. Run batch_writer to preprocess the"
"data and create batch files for imageset" % (repo_dir)
)
# Should have following defined:
req_attributes = [
"global_mean",
"nclass",
"val_start",
"ntrain",
"label_names",
"train_nrec",
"img_size",
"nval",
"train_start",
"val_nrec",
"label_dict",
"batch_prefix",
]
for r in req_attributes:
if r not in dataset_cache:
raise ValueError("Dataset cache missing required attribute %s" % (r))
self.__dict__.update(dataset_cache)
self.filename = os.path.join(repo_dir, self.batch_prefix)
self.center = False if do_transforms else True
self.flip = True if do_transforms else False
self.rgb = rgb
self.multiview = multiview
self.label = "l_id"
if isinstance(self.nclass, dict):
self.nclass = self.nclass[self.label]
# Rough percentage
self.recs_available = getattr(self, self.set_name + "_nrec")
self.macro_start = getattr(self, self.set_name + "_start")
self.macros_available = getattr(self, "n" + self.set_name)
self.ndata = int(self.recs_available * subset_pct / 100.0)
self.start = 0
def load_imagenet_weights(model, path):
# load a pre-trained Alexnet from Neon model zoo to the local
url = 'https://s3-us-west-1.amazonaws.com/nervana-modelzoo/alexnet/old/pre_v1.4.0/'
filename = 'alexnet.p'
size = 488808400
workdir, filepath = Dataset._valid_path_append(path, '', filename)
if not os.path.exists(filepath):
Dataset.fetch_dataset(url, filename, filepath, size)
print 'Loading the Alexnet pre-trained with ImageNet I1K from: ' + filepath
pdict = load_obj(filepath)
param_layers = [l for l in model.layers.layers]
param_dict_list = pdict['model']['config']['layers']
skip_loading = False
for i, layer in enumerate(param_layers):
if not load_pre_trained_weight(i, layer):
skip_loading = True
if not skip_loading:
ps = param_dict_list[i]
print "Loading weights for:{} [src: {}]".format(
layer.name, ps['config']['name'])
layer.load_weights(ps, load_states=True)
else:
config_name = param_dict_list[i]['config']['name'] if i < len(
param_dict_list) else ""
print "Skipped loading weights for: {} [src: {}]".format(
layer.name, config_name)
return
def load_vgg_all_weights(model, path):
# load a pre-trained VGG16 from Neon model zoo to the local
url = 'https://s3-us-west-1.amazonaws.com/nervana-modelzoo/VGG/'
filename = 'VGG_D.p'
size = 554227541
workdir, filepath = Dataset._valid_path_append(path, '', filename)
if not os.path.exists(filepath):
Dataset.fetch_dataset(url, filename, filepath, size)
print('De-serializing the pre-trained VGG16 model...')
pdict = load_obj(filepath)
param_layers = [l for l in model.layers.layers[0].layers]
param_dict_list = pdict['model']['config']['layers']
i = 0
for layer, ps in zip(param_layers, param_dict_list):
i += 1
if i == 43:
break
layer.load_weights(ps, load_states=True)
print(layer.name + " <-- " + ps['config']['name'])
# to load the fc6 and fc7 from caffe into neon fc layers after ROI pooling
neon_fc_layers = model.layers.layers[2].layers[1].layers[0].layers[2:5] +\
model.layers.layers[2].layers[1].layers[0].layers[6:9]
vgg_fc_layers = param_dict_list[44:47] + param_dict_list[48:51]
for layer, ps in zip(neon_fc_layers, vgg_fc_layers):
layer.load_weights(ps, load_states=True)
print(layer.name + " <-- " + ps['config']['name'])
def __init__(self, prm_path=default_prm_path, layer=-4, backend='gpu', cores=32):
print 'Log::Vectorizer:: Initialising Vectorizer'
self.layer = layer
if not os.path.isfile(prm_path):
raise Exception('FileNotFound: Cannot find the file %s' % prm_path)
print 'Log::Vectorizer:: Generating backend, backend: {}'.format(backend)
if backend == 'cpu':
cores = 1
self.cores = cores
self.generated_backend_object = gen_backend(batch_size=self.cores, backend=backend)
self.backend = backend
print 'Log::Vectorizer:: Loading model from %s' % prm_path
model_dict = load_obj(prm_path)
print 'Log::Vectorizer:: Generating model with loaded file'
self.model = Model(model_dict)
# now we are going to extract the middle patch from the image,
# based on the size used to train the model
self.patch_height = model_dict['train_input_shape'][1]
self.patch_width = model_dict['train_input_shape'][2]
print 'Log::Vectorizer:: Initialising Model'
# initialise the model so that internally the arrays are allocated to the correct size
self.model.initialize(model_dict['train_input_shape'])
print 'Log::Vectorizer:: DONE!'
def load_imagenet_weights(model, path):
# load a pre-trained Alexnet from Neon model zoo to the local
url = 'https://s3-us-west-1.amazonaws.com/nervana-modelzoo/alexnet/old/pre_v1.4.0/'
filename = 'alexnet.p'
size = 488808400
workdir, filepath = Dataset._valid_path_append(path, '', filename)
if not os.path.exists(filepath):
Dataset.fetch_dataset(url, filename, filepath, size)
print 'Loading the Alexnet pre-trained with ImageNet I1K from: ' + filepath
pdict = load_obj(filepath)
param_layers = [l for l in model.layers.layers]
param_dict_list = pdict['model']['config']['layers']
skip_loading = False
for i, layer in enumerate(param_layers):
if not load_pre_trained_weight(i, layer):
skip_loading = True
if not skip_loading:
ps = param_dict_list[i]
print "Loading weights for:{} [src: {}]".format(layer.name, ps['config']['name'])
layer.load_weights(ps, load_states=True)
else:
config_name = param_dict_list[i]['config']['name'] if i < len(param_dict_list) else ""
print "Skipped loading weights for: {} [src: {}]".format(layer.name, config_name)
return
def load_vgg_weights(model, path):
url = 'https://s3-us-west-1.amazonaws.com/nervana-modelzoo/'
filename = 'VGG_ILSVRC_16_layers_fc_reduced.p'
size = 244190212
workdir, filepath = Dataset._valid_path_append(path, '', filename)
if not os.path.exists(filepath):
Dataset.fetch_dataset(url, filename, filepath, size)
print(
'De-serializing the pre-trained VGG16 model with dilated convolutions...'
)
pdict = load_obj(filepath)
model_layers = [l for l in model.layers.layers[0].layers]
# convert source model into dictionary with layer name as keys
src_layers = {
layer['config']['name']: layer
for layer in pdict['model']['config']['layers']
}
for layer in model_layers:
if layer.name in src_layers.keys():
layer.load_weights(src_layers[layer.name], load_states=True)
print('{} loaded from source file'.format(layer.name))
elif hasattr(layer, 'W'):
print('Skipping {} layer'.format(layer.name))
def load_imagenet_weights(model, path):
# load a pre-trained Alexnet from Neon model zoo to the local
url = 'https://s3-us-west-1.amazonaws.com/nervana-modelzoo/'
filename = 'alexnet.p'
size = 488808400
workdir, filepath = Dataset._valid_path_append(path, '', filename)
if not os.path.exists(filepath):
Dataset.fetch_dataset(url, filename, filepath, size)
print 'De-serializing the pre-trained Alexnet using ImageNet I1K ...'
pdict = load_obj(filepath)
param_layers = [l for l in model.layers_to_optimize]
param_dict_list = pdict['layer_params_states']
i = 0
for layer, ps in zip(param_layers, param_dict_list):
i = i + 1
print i, layer.name
layer.set_params(ps)
if 'states' in ps:
layer.set_states(ps)
if i == 10:
print 'Only load the pre-trained weights up to conv5 layer of Alexnet'
break
def deserialize(fn, datasets=None, inference=False):
"""
Helper function to load all objects from a serialized file,
this includes callbacks and datasets as well as the model, layers,
etc.
Arguments:
datasets (DataSet, optional): If the dataset is not serialized
in the file it can be passed in
as an argument. This will also
override any dataset in the serialized
file
inference (bool, optional): if true only the weights will be loaded, not
the states
Returns:
Model: the model object
Dataset: the data set object
Callback: the callbacks
"""
config_dict = load_obj(fn)
if datasets is not None:
logger.warn('Ignoring datasets serialized in archive file %s' % fn)
elif 'datasets' in config_dict:
ds_cls = load_class(config_dict['datasets']['type'])
dataset = ds_cls.gen_class(config_dict['datasets']['config'])
datasets = dataset.gen_iterators()
if 'train' in datasets:
data_iter = datasets['train']
else:
key = list(datasets.keys())[0]
data_iter = datasets[key]
logger.warn('Could not find training set iterator'
'using %s instead' % key)
model = Model(config_dict, data_iter)
callbacks = None
if 'callbacks' in config_dict:
# run through the callbacks looking for dataset objects
# replace them with the corresponding data set above
cbs = config_dict['callbacks']['callbacks']
for cb in cbs:
if 'config' not in cb:
cb['config'] = {}
for arg in cb['config']:
if type(cb['config']
[arg]) is dict and 'type' in cb['config'][arg]:
if cb['config'][arg]['type'] == 'Data':
key = cb['config'][arg]['name']
if key in datasets:
cb['config'][arg] = datasets[key]
else:
cb['config'][arg] = None
# now we can generate the callbacks
callbacks = Callbacks.load_callbacks(config_dict['callbacks'], model)
return (model, dataset, callbacks)
def __init__(self,
repo_dir,
inner_size,
do_transforms=True,
rgb=True,
multiview=False,
set_name='train',
subset_pct=100):
assert (subset_pct > 0
and subset_pct <= 100), "subset_pct must be between 0 and 100"
assert (set_name in ['train', 'validation'])
self.set_name = set_name if set_name == 'train' else 'val'
self.repo_dir = repo_dir
self.inner_size = inner_size
self.minibatch_size = self.be.bsz
# Load from repo dataset_cache:
try:
cache_filepath = os.path.join(repo_dir, 'dataset_cache.pkl')
dataset_cache = load_obj(cache_filepath)
except IOError:
raise IOError(
"Cannot find '%s/dataset_cache.pkl'. Run batch_writer to "
"preprocess the data and create batch files for imageset" %
(repo_dir))
# Should have following defined:
req_attributes = [
'global_mean', 'nclass', 'val_start', 'ntrain', 'label_names',
'train_nrec', 'img_size', 'nval', 'train_start', 'val_nrec',
'label_dict', 'batch_prefix'
]
for r in req_attributes:
if r not in dataset_cache:
raise ValueError(
"Dataset cache missing required attribute %s" % (r))
self.__dict__.update(dataset_cache)
self.filename = os.path.join(repo_dir, self.batch_prefix)
self.center = False if do_transforms else True
self.flip = True if do_transforms else False
self.rgb = rgb
self.multiview = multiview
self.label = 'l_id'
if isinstance(self.nclass, dict):
self.nclass = self.nclass[self.label]
# Rough percentage
self.recs_available = getattr(self, self.set_name + '_nrec')
self.macro_start = getattr(self, self.set_name + '_start')
self.macros_available = getattr(self, 'n' + self.set_name)
self.ndata = int(self.recs_available * subset_pct / 100.)
self.start = 0
def deserialize(fn, datasets=None, inference=False):
"""
Helper function to load all objects from a serialized file,
this includes callbacks and datasets as well as the model, layers,
etc.
Arguments:
datasets (DataSet, optional): If the dataset is not serialized
in the file it can be passed in
as an argument. This will also
override any dataset in the serialized
file
inference (bool, optional): if true only the weights will be loaded, not
the states
Returns:
Model: the model object
Dataset: the data set object
Callback: the callbacks
"""
config_dict = load_obj(fn)
if datasets is not None:
logger.warn('Ignoring datasets serialized in archive file %s' % fn)
elif 'datasets' in config_dict:
ds_cls = load_class(config_dict['datasets']['type'])
dataset = ds_cls.gen_class(config_dict['datasets']['config'])
datasets = dataset.gen_iterators()
if 'train' in datasets:
data_iter = datasets['train']
else:
key = datasets.keys()[0]
data_iter = datasets[key]
logger.warn('Could not find training set iterator'
'using %s instead' % key)
model = Model(config_dict, data_iter)
callbacks = None
if 'callbacks' in config_dict:
# run through the callbacks looking for dataset objects
# replace them with the corresponding data set above
cbs = config_dict['callbacks']['callbacks']
for cb in cbs:
if 'config' not in cb:
cb['config'] = {}
for arg in cb['config']:
if type(cb['config'][arg]) is dict and 'type' in cb['config'][arg]:
if cb['config'][arg]['type'] == 'Data':
key = cb['config'][arg]['name']
if key in datasets:
cb['config'][arg] = datasets[key]
else:
cb['config'][arg] = None
# now we can generate the callbacks
callbacks = Callbacks.load_callbacks(config_dict['callbacks'], model)
return (model, dataset, callbacks)
def load_callbacks(cls, cdict, model, data=[]):
if type(cdict) is str:
cdict = load_obj(cdict)
callbacks = cls(model, output_file=cdict['output_file'])
callbacks.epoch_marker = cdict['epoch_marker']
callbacks.callbacks = []
for cb in cdict['callbacks']:
module = load_class(cb['type'])
callbacks.callbacks.append(module(**cb['config']))
return callbacks
def load_callbacks(cls, cdict, model, data=[]):
if type(cdict) is str:
cdict = load_obj(cdict)
callbacks = cls(model, output_file=cdict["output_file"])
callbacks.epoch_marker = cdict["epoch_marker"]
callbacks.callbacks = []
for cb in cdict["callbacks"]:
module = load_class(cb["type"])
callbacks.callbacks.append(module(**cb["config"]))
return callbacks
def run(self):
load_dir = self.image_dir
train_tar = os.path.join(load_dir, 'ILSVRC2012_img_train.tar')
validation_tar = os.path.join(load_dir, 'ILSVRC2012_img_val.tar')
for infile in (train_tar, validation_tar):
if not os.path.exists(infile):
raise IOError(
infile +
" not found. Please ensure you have ImageNet downloaded."
"More info here: http://www.image-net.org/download-imageurls"
)
# download our version of the metadata
meta_dir = load_i1kmeta(self.out_dir)
meta_file = os.path.join(meta_dir, 'neon_ILSVRC2012_devmeta.pkl')
self.meta = load_obj(meta_file)
self.__dict__.update(
self.meta) # get label_dict, label_names, global_mean from meta
self.global_mean = np.mean(self.global_mean.reshape(3, -1),
axis=1).reshape(3, 1)[::-1]
np.random.seed(0)
with tarfile.open(train_tar) as tf:
s_sets = tf.getmembers()
s_tars = [tarfile.open(fileobj=tf.extractfile(s)) for s in s_sets]
print('Building trainset list from synset tars.')
t_jpegfiles = []
totalsz = len(s_tars)
for i, st in enumerate(s_tars):
if i % 100 == 0:
print("%d%% ..." % (int(round((100.0 * i) / totalsz))))
t_jpegfiles += [st.extractfile(m) for m in st.getmembers()]
st.close()
print("Done loading")
np.random.shuffle(t_jpegfiles)
train_labels = [[self.label_dict[j.name[:9]]] for j in t_jpegfiles]
self.train_nrec = len(t_jpegfiles)
self.ntrain = -(-self.train_nrec // self.macro_size)
self.nclass = {'l_id': 1000}
self.train_start = 0
train_labels = {'l_id': np.array(train_labels, dtype=np.int32)}
self.write_batches('train', self.train_start, train_labels,
t_jpegfiles)
with tarfile.open(validation_tar) as tf:
jpegfiles = sorted([tf.extractfile(m) for m in tf.getmembers()],
key=lambda x: x.name)
self.val_nrec = len(jpegfiles)
self.nval = -(-self.val_nrec // self.macro_size)
self.val_start = 10**int(np.log10(self.ntrain) + 1)
val_labels = {
'l_id': np.array(self.val_ground_truth, dtype=np.int32)
}
self.write_batches('val', self.val_start, val_labels, jpegfiles)
self.save_meta()
def read_images(self, split):
"""
Read sentences and image features from pickled dict
Args:
split (str): test or train split
"""
data_path = os.path.join(self.path, 'features.pkl.gz')
self.dataset = load_obj(data_path)
self.sent_data = self.dataset['sents'][split]
self.features = self.dataset['feats']
def read_images(self, split):
"""
Read sentences and image features from pickled dict
Args:
split (str): test or train split
"""
data_path = os.path.join(self.path, "features.pkl.gz")
self.dataset = load_obj(data_path)
self.sent_data = self.dataset["sents"][split]
self.features = self.dataset["feats"]
def load_params(self, param_path):
"""
Loads the model parameters (per layer weights, epochs run, optimizer
states) saved in param_path from serialize().
Arguments:
param_path (str): File containing serialized python dict with layer
weights and states.
"""
pdict = load_obj(param_path)
self.deserialize(pdict)
logger.info('Model weights loaded from %s', param_path)
def read_images(self, split):
"""
Read sentences and image features from pickled dict
Args:
split (str): test or train split
"""
data_path = os.path.join(self.path, 'features.pkl.gz')
from neon.util.persist import load_obj
self.dataset = load_obj(data_path)
self.sent_data = self.dataset['sents'][split]
self.features = self.dataset['feats']
def __init__(self, repo_dir, inner_size,
do_transforms=True, rgb=True, multiview=False,
set_name='train', subset_pct=100):
assert(subset_pct > 0 and subset_pct <= 100), "subset_pct must be between 0 and 100"
assert(set_name in ['train', 'validation'])
self.set_name = set_name if set_name == 'train' else 'val'
self.repo_dir = repo_dir
self.inner_size = inner_size
self.minibatch_size = self.be.bsz
# Load from repo dataset_cache:
try:
cache_filepath = os.path.join(repo_dir, 'dataset_cache.pkl')
dataset_cache = load_obj(cache_filepath)
except IOError:
raise IOError("Cannot find '%s/dataset_cache.pkl'. Run batch_writer to "
"preprocess the data and create batch files for imageset"
% (repo_dir))
# Should have following defined:
req_attributes = ['global_mean', 'nclass', 'val_start', 'ntrain', 'label_names',
'train_nrec', 'img_size', 'nval', 'train_start', 'val_nrec',
'label_dict', 'batch_prefix']
for r in req_attributes:
if r not in dataset_cache:
raise ValueError("Dataset cache missing required attribute %s" % (r))
global_mean = dataset_cache['global_mean']
if global_mean is not None and global_mean.shape != (3, 1):
raise ValueError('Dataset cache global mean is not in the proper format. Run '
'neon/util/update_dataset_cache.py utility on %s.' % cache_filepath)
self.__dict__.update(dataset_cache)
self.filename = os.path.join(repo_dir, self.batch_prefix)
self.center = False if do_transforms else True
self.flip = True if do_transforms else False
self.rgb = rgb
self.multiview = multiview
self.label = 'l_id'
if isinstance(self.nclass, dict):
self.nclass = self.nclass[self.label]
# Rough percentage
self.recs_available = getattr(self, self.set_name + '_nrec')
self.macro_start = getattr(self, self.set_name + '_start')
self.macros_available = getattr(self, 'n' + self.set_name)
self.ndata = int(self.recs_available * subset_pct / 100.)
self.start = 0
def load_params(self, param_path):
"""
Loads the model parameters (per layer weights, epochs run, optimizer
states) saved in param_path from serialize().
Arguments:
param_path (str): File containing serialized python dict with layer
weights and states.
"""
pdict = load_obj(param_path)
self.deserialize(pdict, weights_only=True)
logger.info('Model weights loaded from %s', param_path)
def load_caffe_weights(model, file_path):
pdict = load_obj(file_path)['params']
# we match by name with the caffe blobs
for (pos, layer) in enumerate(model.layers.layers):
if pos == 1: # skip conv4_3
continue
load_weights(layer.layers, pdict)
# we handle the tree-in-tree next
conv4_3_loc = model.layers.layers[1].layers[1].layers[0].layers
conv4_3_conf = model.layers.layers[1].layers[1].layers[1].layers
load_weights(conv4_3_loc, pdict)
load_weights(conv4_3_conf, pdict)
def load_caffe_weights(model, file_path):
pdict = load_obj(file_path)['params']
# we match by name with the caffe blobs
for (pos, layer) in enumerate(model.layers.layers):
if pos == 1: # skip conv4_3
continue
load_weights(layer.layers, pdict)
# we handle the tree-in-tree next
conv4_3_loc = model.layers.layers[1].layers[1].layers[0].layers
conv4_3_conf = model.layers.layers[1].layers[1].layers[1].layers
load_weights(conv4_3_loc, pdict)
load_weights(conv4_3_conf, pdict)
def run(self):
load_dir = self.image_dir
train_tar = os.path.join(load_dir, "ILSVRC2012_img_train.tar")
validation_tar = os.path.join(load_dir, "ILSVRC2012_img_val.tar")
for infile in (train_tar, validation_tar):
if not os.path.exists(infile):
raise IOError(
infile + " not found. Please ensure you have ImageNet downloaded."
"More info here: http://www.image-net.org/download-imageurls"
)
# download our version of the metadata
meta_dir = load_i1kmeta(self.out_dir)
meta_file = os.path.join(meta_dir, "neon_ILSVRC2012_devmeta.pkl")
self.meta = load_obj(meta_file)
self.__dict__.update(self.meta) # get label_dict, label_names, global_mean from meta
self.global_mean = np.mean(self.global_mean.reshape(3, -1), axis=1).reshape(3, 1)[::-1]
np.random.seed(0)
with tarfile.open(train_tar) as tf:
s_sets = tf.getmembers()
s_tars = [tarfile.open(fileobj=tf.extractfile(s)) for s in s_sets]
print("Building trainset list from synset tars.")
t_jpegfiles = []
totalsz = len(s_tars)
for i, st in enumerate(s_tars):
if i % 100 == 0:
print("%d%% ..." % (int(round((100.0 * i) / totalsz))))
t_jpegfiles += [st.extractfile(m) for m in st.getmembers()]
st.close()
print("Done loading")
np.random.shuffle(t_jpegfiles)
train_labels = [[self.label_dict[j.name[:9]]] for j in t_jpegfiles]
self.train_nrec = len(t_jpegfiles)
self.ntrain = -(-self.train_nrec // self.macro_size)
self.nclass = {"l_id": 1000}
self.train_start = 0
train_labels = {"l_id": np.array(train_labels, dtype=np.int32)}
self.write_batches("train", self.train_start, train_labels, t_jpegfiles)
with tarfile.open(validation_tar) as tf:
jpegfiles = sorted([tf.extractfile(m) for m in tf.getmembers()], key=lambda x: x.name)
self.val_nrec = len(jpegfiles)
self.nval = -(-self.val_nrec // self.macro_size)
self.val_start = 10 ** int(np.log10(self.ntrain) + 1)
val_labels = {"l_id": np.array(self.val_ground_truth, dtype=np.int32)}
self.write_batches("val", self.val_start, val_labels, jpegfiles)
self.save_meta()
def load_params(self, param_path, load_states=True):
"""
Loads the model parameters (per layer weights, epochs run, optimizer
states) saved in param_path from serialize().
Arguments:
param_path (str): File containing serialized python dict with layer
weights and states.
load_states (bool): if False, then only the weights will be loaded
into a model in which the layers have already been
created, otherwise will (re)create the layers from
the serialized parameters and set the learning
states as well
"""
self.deserialize(load_obj(param_path), load_states=load_states)
logger.info('Model weights loaded from %s', param_path)
def load_params(self, param_path, load_states=True):
"""
Loads the model parameters (per layer weights, epochs run, optimizer
states) saved in param_path from serialize().
Arguments:
param_path (str): File containing serialized python dict with layer
weights and states.
load_states (bool): if False, then only the weights will be loaded
into a model in which the layers have already been
created, otherwise will (re)create the layers from
the serialized parameters and set the learning
states as well
"""
self.deserialize(load_obj(param_path), load_states=load_states)
logger.info('Model weights loaded from %s', param_path)
def load_weights(self, weight_path):
"""
Loads the layer weights saved in weight_path from serialize().
Arguments:
weight_path (str): File containing serialized python dict with layer
weights and states.
"""
pdict = load_obj(weight_path)
self.epoch_index = pdict['epoch_index']
param_layers = [l for l in self.layers_to_optimize]
param_dict_list = pdict['layer_params_states']
for l, ps in zip(param_layers, param_dict_list):
l.set_params(ps['params'])
if 'states' in ps:
l.set_states(ps['states'])
def load_weights(self, weight_path):
"""
Loads the layer weights saved in weight_path from serialize().
Arguments:
weight_path (str): File containing serialized python dict with layer
weights and states.
"""
pdict = load_obj(weight_path)
self.epoch_index = pdict["epoch_index"]
param_layers = [l for l in self.layers_to_optimize]
param_dict_list = pdict["layer_params_states"]
for l, ps in zip(param_layers, param_dict_list):
l.set_params(ps["params"])
if "states" in ps:
l.set_states(ps["states"])
def load_vgg_weights(model, path):
# load a pre-trained VGG16 from Neon model zoo to the local
url = 'https://s3-us-west-1.amazonaws.com/nervana-modelzoo/VGG/'
filename = 'VGG_D_Conv.p'
size = 169645138
workdir, filepath = Dataset._valid_path_append(path, '', filename)
if not os.path.exists(filepath):
Dataset.fetch_dataset(url, filename, filepath, size)
neon_logger.display('De-serializing the pre-trained VGG16 model...')
pdict = load_obj(filepath)
param_layers = [l for l in model.layers.layers[0].layers[0].layers]
param_dict_list = pdict['model']['config']['layers']
for layer, ps in zip(param_layers, param_dict_list):
neon_logger.display("{}".format(layer.name, ps['config']['name']))
layer.load_weights(ps, load_states=True)
def load_vgg_weights(model, path):
# load a pre-trained VGG16 from Neon model zoo to the local
url = 'https://s3-us-west-1.amazonaws.com/nervana-modelzoo/VGG/'
filename = 'VGG_D_Conv.p'
size = 169645138
workdir, filepath = Dataset._valid_path_append(path, '', filename)
if not os.path.exists(filepath):
Dataset.fetch_dataset(url, filename, filepath, size)
neon_logger.display('De-serializing the pre-trained VGG16 model...')
pdict = load_obj(filepath)
param_layers = [l for l in model.layers.layers[0].layers[0].layers]
param_dict_list = pdict['model']['config']['layers']
for layer, ps in zip(param_layers, param_dict_list):
neon_logger.display("{}".format(layer.name, ps['config']['name']))
layer.load_weights(ps, load_states=True)
def load_weights(self, weight_path):
"""
Loads the layer weights saved in weight_path from serialize().
Arguments:
weight_path (str): File containing serialized python dict with layer
weights and states.
"""
pdict = load_obj(weight_path)
self.epoch_index = pdict['epoch_index']
param_layers = [l for l in self.layers_to_optimize]
param_dict_list = pdict['layer_params_states']
for l, ps in zip(param_layers, param_dict_list):
l.set_params(ps['params'])
if 'states' in ps:
l.set_states(ps['states'])
logger.info('Model weights loaded from %s', weight_path)
def load_imagenet_weights(model, path):
# load a pre-trained Alexnet from Neon model zoo to the local
url = 'https://s3-us-west-1.amazonaws.com/nervana-modelzoo/alexnet/'
filename = 'alexnet.p'
size = 488808400
workdir, filepath = Dataset._valid_path_append(path, '', filename)
if not os.path.exists(filepath):
Dataset.fetch_dataset(url, filename, filepath, size)
print 'De-serializing the pre-trained Alexnet using ImageNet I1K ...'
pdict = load_obj(filepath)
param_layers = [l for l in model.layers.layers[0].layers[0].layers]
param_dict_list = pdict['model']['config']['layers']
for layer, ps in zip(param_layers, param_dict_list):
print layer.name, ps['config']['name']
layer.load_weights(ps, load_states=True)
if ps['config']['name'] == 'Pooling_2':
print 'Only load the pre-trained weights up to conv5 layer of Alexnet'
break
def load_imagenet_weights(model, path):
# load a pre-trained Alexnet from Neon model zoo to the local
url = 'https://s3-us-west-1.amazonaws.com/nervana-modelzoo/alexnet/'
filename = 'alexnet.p'
size = 488808400
workdir, filepath = Dataset._valid_path_append(path, '', filename)
if not os.path.exists(filepath):
Dataset.fetch_dataset(url, filename, filepath, size)
print 'De-serializing the pre-trained Alexnet using ImageNet I1K ...'
pdict = load_obj(filepath)
param_layers = [l for l in model.layers.layers[0].layers[0].layers]
param_dict_list = pdict['model']['config']['layers']
for layer, ps in zip(param_layers, param_dict_list):
print layer.name, ps['config']['name']
layer.load_weights(ps, load_states=True)
if ps['config']['name'] == 'Pooling_2':
print 'Only load the pre-trained weights up to conv5 layer of Alexnet'
break
def get_w2v_vocab(fname, max_vocab_size, cache=True):
"""
Get ordered dict of vocab from google word2vec
"""
if cache:
cache_fname = fname.split('.')[0] + ".vocab"
if os.path.isfile(cache_fname):
vocab, vocab_size = load_obj(cache_fname)
neon_logger.display(
"Word2Vec vocab cached, size is: {}".format(vocab_size))
return vocab, vocab_size
with open(fname, 'rb') as f:
header = f.readline()
vocab_size, embed_dim = map(int, header.split())
binary_len = np.dtype('float32').itemsize * embed_dim
neon_logger.display("Word2Vec vocab size is: {}".format(vocab_size))
vocab_size = min(max_vocab_size, vocab_size)
neon_logger.display("Reducing vocab size to: {}".format(vocab_size))
vocab = OrderedDict()
for i, line in enumerate(range(vocab_size)):
word = []
while True:
ch = f.read(1)
if ch == b' ':
word = (b''.join(word)).decode('utf-8')
break
if ch != b'\n':
word.append(ch)
f.read(binary_len)
vocab[word] = i
if cache:
save_obj((vocab, vocab_size), cache_fname)
return vocab, vocab_size
def load_vgg_weights(model, path):
# load a pre-trained VGG16 from Neon model zoo to the local
url = 'https://s3-us-west-1.amazonaws.com/nervana-modelzoo/VGG/'
filename = 'VGG_D.p'
size = 554227541
workdir, filepath = Dataset._valid_path_append(path, '', filename)
if not os.path.exists(filepath):
Dataset.fetch_dataset(url, filename, filepath, size)
print 'De-serializing the pre-trained VGG16 model...'
pdict = load_obj(filepath)
param_layers = [l for l in model.layers.layers[0].layers[0].layers]
param_dict_list = pdict['model']['config']['layers']
i = 0
for layer, ps in zip(param_layers, param_dict_list):
i += 1
print layer.name, ps['config']['name']
layer.load_weights(ps, load_states=True)
if i == 43:
break
def get_w2v_vocab(fname, max_vocab_size, cache=True):
"""
Get ordered dict of vocab from google word2vec
"""
if cache:
cache_fname = fname.split('.')[0] + ".vocab"
if os.path.isfile(cache_fname):
vocab, vocab_size = load_obj(cache_fname)
neon_logger.display("Word2Vec vocab cached, size is: {}".format(vocab_size))
return vocab, vocab_size
with open(fname, 'rb') as f:
header = f.readline()
vocab_size, embed_dim = map(int, header.split())
binary_len = np.dtype('float32').itemsize * embed_dim
neon_logger.display("Word2Vec vocab size is: {}".format(vocab_size))
vocab_size = min(max_vocab_size, vocab_size)
neon_logger.display("Reducing vocab size to: {}".format(vocab_size))
vocab = OrderedDict()
for i, line in enumerate(range(vocab_size)):
word = []
while True:
ch = f.read(1)
if ch == b' ':
word = (b''.join(word)).decode('utf-8')
break
if ch != b'\n':
word.append(ch)
f.read(binary_len)
vocab[word] = i
if cache:
save_obj((vocab, vocab_size), cache_fname)
return vocab, vocab_size
def load_vgg_weights(model, path):
# load a pre-trained VGG16 from Neon model zoo to the local
url = 'https://s3-us-west-1.amazonaws.com/nervana-modelzoo/VGG/'
filename = 'VGG_D.p'
size = 554227541
workdir, filepath = Dataset._valid_path_append(path, '', filename)
if not os.path.exists(filepath):
Dataset.fetch_dataset(url, filename, filepath, size)
print 'De-serializing the pre-trained VGG16 model...'
pdict = load_obj(filepath)
param_layers = [l for l in model.layers.layers[0].layers[0].layers]
param_dict_list = pdict['model']['config']['layers']
i = 0
for layer, ps in zip(param_layers, param_dict_list):
i += 1
print layer.name, ps['config']['name']
layer.load_weights(ps, load_states=True)
if i == 43:
break
def load_vgg_weights(model, path):
url = 'https://s3-us-west-1.amazonaws.com/nervana-modelzoo/VGG/'
filename = 'VGG_D_Conv_fused_conv_bias.p'
size = 58867537
workdir, filepath = Dataset._valid_path_append(path, '', filename)
if not os.path.exists(filepath):
Dataset.fetch_dataset(url, filename, filepath, size)
print('De-serializing the pre-trained VGG16 model...')
pdict = load_obj(filepath)
param_layers = [l for l in model.layers.layers[0].layers]
param_dict_list = pdict['model']['config']['layers']
i = 0
for layer, ps in zip(param_layers, param_dict_list):
# finished loading param_dict_list[00 - 29] and param_layers[00-29]
if i == 30:
break
layer.load_weights(ps, load_states=False)
i += 1
print(layer.name + " <-- " + ps['config']['name'])
def load_vgg_weights(model, path):
url = 'https://s3-us-west-1.amazonaws.com/nervana-modelzoo/VGG/'
filename = 'VGG_D_Conv_fused_conv_bias.p'
size = 58867537
workdir, filepath = Dataset._valid_path_append(path, '', filename)
if not os.path.exists(filepath):
Dataset.fetch_dataset(url, filename, filepath, size)
print('De-serializing the pre-trained VGG16 model...')
pdict = load_obj(filepath)
param_layers = [l for l in model.layers.layers[0].layers]
param_dict_list = pdict['model']['config']['layers']
i = 0
for layer, ps in zip(param_layers, param_dict_list):
# finished loading param_dict_list[00 - 29] and param_layers[00-29]
if i == 30:
break
layer.load_weights(ps, load_states=False)
i += 1
print(layer.name + " <-- " + ps['config']['name'])
def __init__(self, pdict):
if type(pdict) is str:
pdict = load_obj(pdict)
super(ModelDescription, self).__init__(pdict)
#!/usr/bin/env python
# ----------------------------------------------------------------------------
# Copyright 2016 Nervana Systems Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ----------------------------------------------------------------------------
from neon.util.argparser import NeonArgparser
from neon.util.persist import load_obj
from neon.transforms import Misclassification
from neon.models import Model
from neon.data import ImageLoader
# parse the command line arguments (generates the backend)
parser = NeonArgparser(__doc__)
args = parser.parse_args()
# setup data provider
test_set = ImageLoader(set_name='validation', repo_dir=args.data_dir,
inner_size=32, scale_range=40, do_transforms=False)
model = Model(load_obj(args.model_file), test_set)
print 'Accuracy: %.1f %% (Top-1)' % (1.0-model.eval(test_set, metric=Misclassification())*100)
def __init__(self, path='.', n_mb=None, img_per_batch=None, conv_size=None,
rpn_rois_per_img=None, frcn_rois_per_img=None, add_flipped=False,
shuffle=False, deterministic=False, rebuild_cache=False, subset_pct=100,
mock_db=None):
self.batch_index = 0
self.path = path
self.mock_db = mock_db
# how many ROIs per image
self.rois_per_img = rpn_rois_per_img if rpn_rois_per_img else self.RPN_ROI_PER_IMAGE
self.img_per_batch = img_per_batch if img_per_batch else self.IMG_PER_BATCH
self.rois_per_batch = self.rois_per_img * self.img_per_batch
# how many ROIs to use to train frcnn
self.frcn_rois_per_img = frcn_rois_per_img if frcn_rois_per_img \
else self.FRCNN_ROI_PER_IMAGE
assert self.img_per_batch == 1, "Only a minibatch of 1 is supported."
self.num_classes = len(self.CLASSES)
self._class_to_index = dict(list(zip(self.CLASSES, list(range(self.num_classes)))))
# shape of the final conv layer
if conv_size:
self._conv_size = conv_size
else:
self._conv_size = int(np.floor(self.MAX_SIZE * self.SCALE))
self._feat_stride = 1 / float(self.SCALE)
self._num_scales = len(self.SCALES) * len(self.RATIOS)
self._total_anchors = self._conv_size * self._conv_size * self._num_scales
self.shuffle = shuffle
self.deterministic = deterministic
self.add_flipped = add_flipped
# load the configure the dataset paths
self.config = self.load_data()
# annotation metadata
self._annotation_file_ext = '.xml'
self._annotation_obj_tag = 'object'
self._annotation_class_tag = 'name'
self._annotation_xmin_tag = 'xmin'
self._annotation_xmax_tag = 'xmax'
self._annotation_ymin_tag = 'ymin'
self._annotation_ymax_tag = 'ymax'
# self.rois_per_batch is 128 (2*64) ROIs
# But the image path batch size is self.img_per_batch
# need to control the batch size here
assert self.img_per_batch is 1, "Only a batch size of 1 image is supported"
neon_logger.display("Backend batchsize is changed to be {} "
"from Object Localization dataset".format(
self.img_per_batch))
self.be.bsz = self.img_per_batch
# 0. allocate buffers
self.allocate()
if not self.mock_db:
# 1. read image index file
assert os.path.exists(self.config['image_path']), \
'Image index file does not exist: {}'.format(self.config['image_path'])
with open(self.config['index_path']) as f:
self.image_index = [x.strip() for x in f.readlines()]
num_images = len(self.image_index)
self.num_image_entries = num_images * 2 if self.add_flipped else num_images
self.ndata = self.num_image_entries * self.rois_per_img
else:
self.num_image_entries = 1
self.ndata = self.num_image_entries * self.rois_per_img
assert (subset_pct > 0 and subset_pct <= 100), ('subset_pct must be between 0 and 100')
if n_mb is not None:
self.nbatches = n_mb
else:
self.nbatches = int(self.num_image_entries / self.img_per_batch * subset_pct / 100)
self.cache_file = self.config['cache_path']
if os.path.exists(self.cache_file) and not rebuild_cache and not self.mock_db:
self.roi_db = load_obj(self.cache_file)
neon_logger.display('ROI dataset loaded from file {}'.format(self.cache_file))
elif not self.mock_db:
# 2. read object Annotations (XML)
roi_db = self.load_roi_groundtruth()
if(self.add_flipped):
roi_db = self.add_flipped_db(roi_db)
# 3. construct acnhor targets
self.roi_db = self.add_anchors(roi_db)
if NORMALIZE_BBOX_TARGETS:
# 4. normalize bbox targets by class
self.roi_db = self.normalize_bbox_targets(self.roi_db)
save_obj(self.roi_db, self.cache_file)
neon_logger.display('wrote ROI dataset to {}'.format(self.cache_file))
else:
assert self.mock_db is not None
roi_db = [self.mock_db]
self.roi_db = self.add_anchors(roi_db)
# 4. map anchors back to full canvas.
# This is neccessary because the network outputs reflect the full canvas.
# We cache the files in the unmapped state (above) to save memory.
self.roi_db = unmap(self.roi_db)
def configure(self, repo_dir, inner_size, do_transforms,
rgb, multiview, set_name, subset_pct, macro):
"""
Set up all dataset config options.
"""
assert (subset_pct > 0 and subset_pct <= 100), (
'subset_pct must be between 0 and 100')
assert(set_name in ['train', 'validation'])
self.set_name = set_name if set_name == 'train' else 'val'
self.repo_dir = repo_dir
self.inner_size = inner_size
self.minibatch_size = self.be.bsz
self.center = not do_transforms
self.flip = do_transforms
self.rgb = rgb
self.multiview = multiview
self.start = 0
self.macro = macro
if not macro:
self.filename = os.path.join(repo_dir, 'filelist.txt')
if not os.path.exists(self.filename):
raise IOError('Cannot find %s' % self.filename)
filelist = np.genfromtxt(self.filename, dtype=str)
self.ndata = int(len(filelist) * subset_pct / 100.)
assert self.ndata != 0
self.macro_start = 0
self.nlabels = 1
self.nclass = 1
self.global_mean = None
self.img_size = 256
return
# Load from repo dataset_cache:
try:
cache_filepath = os.path.join(repo_dir, 'dataset_cache.pkl')
dataset_cache = load_obj(cache_filepath)
except IOError:
raise IOError("Cannot find '%s/dataset_cache.pkl'. Run batch_writer "
"to preprocess the data and create batch files for "
"imageset" % (repo_dir))
# Should have following defined:
req_attributes = ['global_mean', 'nclass', 'val_start', 'ntrain',
'label_names', 'train_nrec', 'img_size', 'nval',
'train_start', 'val_nrec', 'label_dict',
'batch_prefix']
for r in req_attributes:
if r not in dataset_cache:
raise ValueError(
'Dataset cache missing required attribute %s' % (r))
if dataset_cache['global_mean'].shape != (3, 1):
raise ValueError('Dataset cache global mean is not in the proper format. Run '
'neon/util/update_dataset_cache.py utility on %s.' % cache_filepath)
self.__dict__.update(dataset_cache)
self.filename = os.path.join(repo_dir, self.batch_prefix)
self.label = 'l_id'
if isinstance(self.nclass, dict):
self.nclass = self.nclass[self.label]
self.recs_available = getattr(self, self.set_name + '_nrec')
self.macro_start = getattr(self, self.set_name + '_start')
self.macros_available = getattr(self, 'n' + self.set_name)
self.ndata = int(self.recs_available * subset_pct / 100.)
def __init__(self, pdict):
if type(pdict) is str:
pdict = load_obj(pdict)
super(ModelDescription, self).__init__(pdict)
# fit the model for 3 epochs
model.fit(train,
optimizer=opt,
num_epochs=3,
cost=cost,
callbacks=callbacks)
train.reset()
# get 1 image
for im, l in train:
break
train.exit_batch_provider()
save_obj((im.get(), l.get()), 'im1.pkl')
im_save = im.get().copy()
if args.resume:
(im2, l2) = load_obj('im1.pkl')
im.set(im2)
l.set(l2)
# run fprop and bprop on this minibatch save the results
out_fprop = model.fprop(im)
out_fprop_save = [x.get() for x in out_fprop]
im.set(im_save)
out_fprop = model.fprop(im)
out_fprop_save2 = [x.get() for x in out_fprop]
for x, y in zip(out_fprop_save, out_fprop_save2):
assert np.max(np.abs(x - y)) == 0.0, '2 fprop iterations do not match'
# run fit fot 1 minibatch
# have to do this by hand
def __init__(self,
image_set,
year,
path='.',
add_flipped=False,
overlap_thre=None,
output_type=0,
n_mb=None,
img_per_batch=None,
rois_per_img=None,
rois_random_sample=True,
shuffle=False):
self.isRoiDB = True
self.batch_index = 0
self.year = year
self.image_set = image_set
self.add_flipped = add_flipped
self.overlap_thre = overlap_thre if overlap_thre else FRCN_IOU_THRE
self.output_type = output_type
# how many ROIs per image
self.rois_per_image = rois_per_img if rois_per_img else FRCN_ROI_PER_IMAGE
self.img_per_batch = img_per_batch if img_per_batch else FRCN_IMG_PER_BATCH
self.fg_rois_per_image = FRCN_FG_FRAC * self.rois_per_image
self.bg_rois_per_image = self.rois_per_image - self.fg_rois_per_image
self.rois_per_batch = self.rois_per_image * self.img_per_batch
self.rois_random_sample = rois_random_sample
self.shuffle = shuffle
self.cache_file_name = 'voc_{}_{}_flip_{}_ovlp_{}.pkl'.format(
self.year, self.image_set, self.add_flipped, self.overlap_thre)
print 'prepare PASCAL VOC {} from year {}: add flipped image {} and overlap threshold {}'\
.format(self.image_set, self.year, self.add_flipped, self.overlap_thre)
# PASCAL class to index
self.num_classes = PASCAL_VOC_NUM_CLASSES
self._class_to_index = dict(
zip(PASCAL_VOC_CLASSES, xrange(self.num_classes)))
# load the voc dataset
self.voc_root = self.load_voc(image_set, year, path)
self.cache_file = os.path.join(self.voc_root, self.cache_file_name)
# load the precomputed ss results from voc data, it includes both 2007 and 2012 data
self.ss_path = self.load_voc('ss', None, path)
# VOC paths and infos
self.image_index_file = os.path.join(self.voc_root, 'ImageSets',
'Main', self.image_set + '.txt')
self.image_path = os.path.join(self.voc_root, 'JPEGImages')
self._image_file_ext = '.jpg'
self.annotation_path = os.path.join(self.voc_root, 'Annotations')
self._annotation_file_ext = '.xml'
self._annotation_obj_tag = 'object'
self._annotation_class_tag = 'name'
self._annotation_xmin_tag = 'xmin'
self._annotation_xmax_tag = 'xmax'
self._annotation_ymin_tag = 'ymin'
self._annotation_ymax_tag = 'ymax'
self._selective_search_ext = '.pkl'
self.selective_search_file = os.path.join(
self.ss_path,
'_'.join(['voc', year, self.image_set, 'selectivesearch.pkl']))
self._bb_xmin_idx = 0
self._bb_ymin_idx = 1
self._bb_xmax_idx = 2
self._bb_ymax_idx = 3
# self.rois_per_batch is 128 (2*64) ROIs
# But the image path batch size is self.img_per_batch
# need to control the batch size here
print "Backend batchsize is changed to be image_per_batch from PASCAL_VOC dataset"
self.be.bsz = self.img_per_batch
# backend tensor to push the data
self.image_shape = (3, FRCN_MAX_SCALE, FRCN_MAX_SCALE)
self.img_np = np.zeros(
(3, FRCN_MAX_SCALE, FRCN_MAX_SCALE, self.be.bsz), dtype=np.float32)
self.dev_X_img = self.be.iobuf(self.image_shape, dtype=np.float32)
self.dev_X_img_chw = self.dev_X_img.reshape(3, FRCN_MAX_SCALE,
FRCN_MAX_SCALE,
self.be.bsz)
# for rois, features are 4 + 1 (idx within the batch)
self.dev_X_rois = self.be.zeros((self.rois_per_batch, 5))
self.dev_y_labels_flat = self.be.zeros((1, self.rois_per_batch),
dtype=np.int32)
self.dev_y_labels = self.be.zeros(
(self.num_classes, self.rois_per_batch), dtype=np.int32)
self.dev_y_bbtargets = self.be.zeros(
(self.num_classes * 4, self.rois_per_batch))
self.dev_y_bbmask = self.be.zeros(
(self.num_classes * 4, self.rois_per_batch))
# the shape will indicate the shape for 1st path (ImageNet model), and
# 2nd path (ROIs)
self.shape = [self.image_shape, self.num_classes * 4]
# Need to do the following:
# 1. load the image index list
# 2. for each image, load the ground truth from pascal annotation
# 3. load the selective search ROIs (this step needs gt ROIs)
# 4.1. merge the ROIs
# 4.2. may have to add the flipped images for training
# 4.3. add the fields for max overlap and max overlapped classes
# 4.4. add the bounding box targets for regression
# 5. during minibatch feeding:
# - rescale images
# - rescale ROIs
# - random select foreground ROIs (bigger ones)
# - random select background ROIS (smaller ones)
# - clamp bg ROI labels (to be 0)
# - convert ROIs into the regression target (ROIs, 4*21)
# 1.
assert os.path.exists(self.image_index_file), \
'Image index file does not exist: {}'.format(self.image_index_file)
with open(self.image_index_file) as f:
self.image_index = [x.strip() for x in f.readlines()]
# self.image_index = image_index * 2 if self.add_flipped else image_index
self.num_images = len(self.image_index)
self.num_image_entries = self.num_images * \
2 if self.add_flipped else self.num_images
self.ndata = self.num_image_entries * self.rois_per_image
self.nbatches = self.num_image_entries / self.img_per_batch
if n_mb is not None:
self.nbatches = n_mb
if os.path.exists(self.cache_file):
self.roi_db = load_obj(self.cache_file)
print 'ROI dataset loaded from file {}'.format(self.cache_file)
else:
# 2.
self.roi_gt = self.load_pascal_roi_groundtruth()
# 3.
self.roi_ss = self.load_pascal_roi_selectivesearch()
# 4.
self.roi_db = self.combine_gt_ss_roi()
save_obj(self.roi_db, self.cache_file)
print 'wrote ROI dataset to {}'.format(self.cache_file)
from neon.util.argparser import NeonArgparser
from neon.layers import Pooling
from neon.models import Model
from neon.data import ImageLoader
from neon.util.persist import save_obj, load_obj
# parse the command line arguments (generates the backend)
parser = NeonArgparser(__doc__)
args = parser.parse_args()
scales = [112, 128, 160, 240]
for scale in scales:
print scale
test = ImageLoader(set_name='validation', shuffle=False, do_transforms=False, inner_size=scale,
scale_range=scale, repo_dir=args.data_dir)
model_desc = load_obj(args.model_file)
model_desc['model']['config']['layers'].insert(-1, Pooling('all', op='avg').get_description())
model = Model(model_desc, test, inference=True)
softmaxes = model.get_outputs(test)
save_obj(softmaxes, "bigfeat_dropout_SM_{}.pkl".format(scale))
import os from neon.util.argparser import NeonArgparser from neon.util.persist import load_obj from neon.transforms import Misclassification, CrossEntropyMulti from neon.optimizers import GradientDescentMomentum from neon.layers import GeneralizedCost from neon.models import Model from neon.data import DataLoader, ImageParams # parse the command line arguments (generates the backend) parser = NeonArgparser(__doc__) args = parser.parse_args() # setup data provider test_dir = os.path.join(args.data_dir, 'val') shape = dict(channel_count=3, height=32, width=32) test_params = ImageParams(center=True, flip=False, **shape) common = dict(target_size=1, nclasses=10) test_set = DataLoader(set_name='val', repo_dir=test_dir, media_params=test_params, **common) model = Model(load_obj(args.model_file)) cost = GeneralizedCost(costfunc=CrossEntropyMulti()) opt = GradientDescentMomentum(0.1, 0.9, wdecay=0.0001) model.initialize(test_set, cost=cost) acc = 1.0 - model.eval(test_set, metric=Misclassification())[0] print 'Accuracy: %.1f %% (Top-1)' % (acc*100.0) model.benchmark(test_set, cost=cost, optimizer=opt)
# hyperparameters from the reference
args.batch_size = 64
embed_dim = 620
valid_split = None
# setup backend
be = gen_backend(**extract_valid_args(args, gen_backend))
# load the documents by giving the path and what extension the files are
data_file, vocab_file = load_data(args.data_dir, valid_split=valid_split,
max_vocab_size=args.max_vocab_size,
max_len_w=args.max_len_w,
output_path=args.output_dir,
file_ext=['txt'],
subset_pct=args.subset_pct)
vocab, rev_vocab, word_count = load_obj(vocab_file)
vocab_size = len(vocab)
neon_logger.display("\nData loading complete.")
neon_logger.display("\nVocab size from the dataset is: {}".format(vocab_size))
index_from = 2 # 0: padding 1: oov
vocab_size_layer = vocab_size + index_from
init_embed_dev = Uniform(low=-0.1, high=0.1)
# sent2vec network
nhidden = 2400
gradient_clip_norm = 5.0
train_set = SentenceHomogenous(data_file=data_file, sent_name='train', text_name='report_train',
layers.append(Conv(name = 'Custom Head 2', **conv_params(1, 2, relu=False)))
layers.append(Activation(Softmax()))
# layers.append(Affine(512, init=Kaiming(local=False),
# batch_norm=True, activation=Rectlin()))
# layers.append(Affine(2, init=Kaiming(local=False), activation=Softmax()))
return Model(layers=layers)
lunaModel = create_network(args.depth)
PRETRAINED = False
# Pre-trained ResNet 50
# It assumes the image has a depth channel of 3
pretrained_weights_file = 'resnet{}_weights.prm'.format(args.depth)
print ('Loading pre-trained ResNet weights: {}'.format(pretrained_weights_file))
trained_resnet = load_obj(pretrained_weights_file) # Load a pre-trained resnet 50 model
# Load the pre-trained weights to our model
param_layers = [l for l in lunaModel.layers.layers]
param_dict_list = trained_resnet['model']['config']['layers']
for layer, params in zip(param_layers, param_dict_list):
if (layer.name == 'end_resnet'):
break
# ResNet is trained on images that have 3 color depth channels
# Our data usually isn't 3 color channels so we should not load the weights for that layer
if (layer.name != 'Input Layer'):
layer.load_weights(params, load_states=False) # Don't load the state, just load the weights
from neon.util.persist import load_obj, save_obj
from neon import logger as neon_logger
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("cache_file", help="path to data cache file")
args = parser.parse_args()
cache_file = args.cache_file
# check for RW access to file
assert os.path.exists(cache_file), "file does not exist %s" % cache_file
if not os.access(os.path.abspath(cache_file), os.R_OK | os.W_OK):
raise IOError("Need to add read and/or write permissions on file %s" % cache_file)
dc = load_obj(cache_file)
if "global_mean" not in dc or "img_size" not in dc:
raise ValueError("data cache file missing global_mean key")
sz = dc["img_size"]
gm = dc["global_mean"]
if len(gm.shape) != 2 or (gm.shape[0] != sz * sz * 3 or gm.shape[1] != 1):
raise ValueError("global mean shape {} does not match format expected".format(gm.shape))
# Collapse the full tensor mean into channel means and correct the order (RGB <-> BGR)
dc["global_mean"] = np.mean(gm.reshape(3, -1), axis=1).reshape(3, 1)[::-1]
save_obj(dc, cache_file)
def load_pascal_roi_selectivesearch(self):
"""
Load the pre-computed selective search data on PASCAL VOC in pickle file
The pickle file contains images and rp:
images: image indices for the dataset (Img, 1)
name in string is in images[i][0][0]
rp: all the proposed ROIs for each image (Img, 1)
in bb[i], there are (B, 4) for B proposed ROIs
The coordinates are ordered as:
[y1, x1, y2, x2]
While ground truth coordinates are:
[x1, y1, x2, y2]
So it needs re-ordering
"""
assert self.roi_gt is not None, 'Ground truth ROIs need to be loaded first'
assert os.path.exists(self.selective_search_file), \
'selected search data does not exist'
ss_data = load_obj(self.selective_search_file)
ss_bb = ss_data['boxes'].ravel()
ss_img_idx = ss_data['images'].ravel()
ss_num_img = ss_bb.shape[0]
assert ss_num_img == self.num_images, \
'Number of images in SS data must match number of image in the dataset'
roi_ss = []
# load the bb from SS and compare with gt
for i in xrange(ss_num_img):
# make sure the image index match
assert self.image_index[i] == ss_img_idx[i][0]
bb = (ss_bb[i][:, (1, 0, 3, 2)] - 1)
num_boxes = bb.shape[0]
overlaps = np.zeros((num_boxes, self.num_classes),
dtype=np.float32)
gt_bb = self.roi_gt[i]['gt_bb']
gt_classes = self.roi_gt[i]['gt_classes'].ravel()
gt_overlap, gt_dim = calculate_bb_overlap(bb.astype(np.float),
gt_bb.astype(np.float))
max_overlap_area = gt_overlap.max(axis=gt_dim)
max_overlap_arg = gt_overlap.argmax(axis=gt_dim)
# only put the non-zero overlaps into the table
I = np.where(max_overlap_area > 0)[0]
overlaps[I, gt_classes[max_overlap_arg[I]]] = max_overlap_area[I]
max_overlap_class = overlaps.argmax(axis=gt_dim)
max_overlaps = overlaps.max(axis=gt_dim)
# prepare the bounding box targets
ss_bb_targets = np.zeros((num_boxes, 5), np.float32)
# only the ones with large enough overlap with gt are used
use_idx = np.where(max_overlaps >= self.overlap_thre)[0]
bb_targets = self._compute_bb_targets(
gt_bb[max_overlap_arg[use_idx]], bb[use_idx],
max_overlap_class[use_idx])
ss_bb_targets[use_idx] = bb_targets
roi_ss.append({
'ss_bb':
bb,
'gt_classes':
np.zeros((num_boxes, 1), dtype=np.int32),
'gt_overlaps':
overlaps,
'max_overlap_area':
max_overlap_area.reshape(-1, 1),
'max_overlap_class':
max_overlap_class.reshape(-1, 1),
'bb_targets':
ss_bb_targets,
})
return roi_ss
# hyperparameters from the reference
args.batch_size = 64
embed_dim = 620
valid_split = None
# setup backend
be = gen_backend(**extract_valid_args(args, gen_backend))
# load the documents by giving the path and what extension the files are
data_file, vocab_file = load_data(args.data_dir, valid_split=valid_split,
max_vocab_size=args.max_vocab_size,
max_len_w=args.max_len_w,
output_path=args.output_dir,
file_ext=['txt'],
subset_pct=args.subset_pct)
vocab, rev_vocab, word_count = load_obj(vocab_file)
vocab_size = len(vocab)
neon_logger.display("\nData loading complete.")
neon_logger.display("\nVocab size from the dataset is: {}".format(vocab_size))
index_from = 2 # 0: padding 1: oov
vocab_size_layer = vocab_size + index_from
init_embed_dev = Uniform(low=-0.1, high=0.1)
# sent2vec network
nhidden = 2400
gradient_clip_norm = 5.0
train_set = SentenceHomogenous(data_file=data_file, sent_name='train', text_name='report_train',
nclasses=c)
data_dir = args.image_path
test_set = PixelWiseImageLoader(set_name='test',
repo_dir=data_dir,
media_params=test_params,
index_file=os.path.join(
data_dir, 'test_images.csv'),
**common)
# initialize model object
segnet_model = Model(layers=gen_model(c, h, w))
segnet_model.initialize(test_set)
# load up the serialized model
model_desc = ModelDescription(load_obj(args.save_model_file))
for layer in segnet_model.layers_to_optimize:
name = layer.name
trained_layer = model_desc.getlayer(name)
layer.load_weights(trained_layer)
fig = plt.figure()
if args.display:
plt.ion()
im1 = None
im2 = None
cnt = 1
for x, t in test_set:
z = segnet_model.fprop(x).get()
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ----------------------------------------------------------------------------
from neon.util.argparser import NeonArgparser
from neon.util.persist import load_obj
from neon.transforms import Misclassification
from neon.models import Model
from neon.data import ImageLoader
# parse the command line arguments (generates the backend)
parser = NeonArgparser(__doc__)
args = parser.parse_args()
# setup data provider
test_set = ImageLoader(set_name='validation',
repo_dir=args.data_dir,
inner_size=32,
scale_range=40,
do_transforms=False)
model = Model(load_obj(args.model_file))
print 'Accuracy: %.1f %% (Top-1)' % (
1.0 - model.eval(test_set, metric=Misclassification()) * 100)