FeatureExtraction.py
# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license. See LICENSE.md file in the project root
# for full license information.
# ==============================================================================
from __future__ import print_function
import os
import numpy as np
import cntk as C
from cntk import load_model, combine
import cntk.io.transforms as xforms
from cntk.logging import graph
from cntk.logging.graph import get_node_outputs
def create_mb_source(image_height, image_width, num_channels, map_file):
transforms = [xforms.scale(width=image_width, height=image_height, channels=num_channels, interpolations='linear')]
return C.io.MinibatchSource(
C.io.ImageDeserializer(map_file, C.io.StreamDefs(
features=C.io.StreamDef(field='image', transforms=transforms),
labels=C.io.StreamDef(field='label', shape=1000))),
randomize=False)
def eval_and_write(model_file, node_name, output_file, minibatch_source, num_objects):
# load model and pick desired node as output
loaded_model = load_model(model_file)
node_in_graph = loaded_model.find_by_name(node_name)
output_nodes = combine([node_in_graph.owner])
# evaluate model and get desired node output
print("Evaluating model for output node %s" % node_name)
features_si = minibatch_source['features']
with open(output_file, 'wb') as results_file:
for i in range(0, num_objects):
mb = minibatch_source.next_minibatch(1)
output = output_nodes.eval(mb[features_si])
# write results to file
out_values = output[0].flatten()
np.savetxt(results_file, out_values[np.newaxis], fmt="%.6f")
if __name__ == '__main__':
# define location of model and data and check existence
base_folder = os.path.dirname(os.path.abspath(__file__))
model_file = os.path.join(base_folder, "..", "..", "..", "PretrainedModels", "ResNet18_ImageNet_CNTK.model")
map_file = os.path.join(base_folder, "..", "DataSets", "Grocery", "test.txt")
os.chdir(os.path.join(base_folder, "..", "DataSets", "Grocery"))
if not (os.path.exists(model_file) and os.path.exists(map_file)):
print("Please run 'python install_data_and_model.py' first to get the required data and model.")
exit(0)
# create minibatch source
image_height = 224
image_width = 224
num_channels = 3
minibatch_source = create_mb_source(image_height, image_width, num_channels, map_file)
# use this to print all node names of the model (and knowledge of the model to pick the correct one)
# node_outputs = get_node_outputs(load_model(model_file))
# for out in node_outputs: print("{0} {1}".format(out.name, out.shape))
# use this to get 1000 class predictions (not yet softmaxed!)
# node_name = "z"
# output_file = os.path.join(base_folder, "predOutput.txt")
# use this to get 512 features from the last but one layer of ResNet_18
node_name = "z.x"
output_file = os.path.join(base_folder, "layerOutput.txt")
# evaluate model and write out the desired layer output
eval_and_write(model_file, node_name, output_file, minibatch_source, num_objects=5)
print("Done. Wrote output to %s" % output_file)