LSTM cudnn test
This commit is contained in:
Francesco Gatti
+59
-94
@@ -5,96 +5,51 @@ import numpy as np
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import argparse
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import tensorflow as tf
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import os
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import msgpack
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import lmdb
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import random
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import struct
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from keras.models import Sequential, Model
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def export_dense(name, weights, bias):
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print "######## EXPORT", name, "LAYER ########"
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print "Original weighs:"
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print weights
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print bias, "\n"
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def bin_write(f, data):
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data = data.flatten()
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fmt = 'f'*len(data)
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bin = struct.pack(fmt, *data)
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f.write(bin)
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#input, filters
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I, C = np.shape(weights)
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B = np.shape(bias)
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print "w shape: ", I, C
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print "b shape: ", B
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def export_layer(name, weights, bias):
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print ("######## EXPORT", name, "LAYER ########")
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wgs = [ [ j[i] for j in weights ] for i in xrange(C) ]
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wgs = np.array(wgs, dtype=np.float32)
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print "REPOSITIONED WEIGHTS:"
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print wgs
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print("wgs pretranpose: ", np.shape(weights))
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# convert NHWC to NCHW
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if(weights.ndim == 4):
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weights = weights.transpose(3,2,0,1)
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elif(weights.ndim == 3):
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weights = weights.transpose(2,1,0)
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else:
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print("Ndim", weights.ndim)
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raise("not implemented with dim" )
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print("weights: ", np.shape(weights))
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print("bias: ", np.shape(bias))
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weights = np.array(weights.flatten(), dtype=np.float32)
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bias = np.array(bias, dtype=np.float32)
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wgs.tofile(name + ".bin", format="f")
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bias.tofile(name + ".bias.bin", format="f")
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print "WEIGHTS saved\n"
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print(len(weights) + len(bias))
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def export_conv2d(name, weights, bias):
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print "######## EXPORT", name, "LAYER ########"
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print "Original weighs:"
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print weights
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print bias, "\n"
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# height, width, input, filters
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H, W, N, C = np.shape(weights)
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B = np.shape(bias)
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print "w shape: ", N, C, H, W
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print "b shape: ", B
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f = open(name + ".bin", mode='wb')
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bin_write(f, weights)
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bin_write(f, bias)
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print ("WEIGHTS saved\n")
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wgs = weights.transpose()
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wgs = wgs.transpose(0, 1, 3, 2)
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print "Final shape:", np.shape(wgs)
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wgs = np.array(wgs.flatten(), dtype=np.float32)
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print "REPOSITIONED WEIGHTS:"
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print wgs
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bias = np.array(bias, dtype=np.float32)
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wgs.tofile(name + ".bin", format="f")
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bias.tofile(name + ".bias.bin", format="f")
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print "WEIGHTS saved\n"
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def export_conv3d(name, weights, bias):
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print "######## EXPORT", name, "LAYER ########"
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print "Original weighs:"
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print weights
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print bias, "\n"
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print np.shape(weights)
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# height, width, input, thickness, filters
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H, W, T, N, C = np.shape(weights)
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B = np.shape(bias)
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print "w shape: ", T, C, H, W #thickness is number of images for cudnn
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print "b shape: ", B
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wgs = weights.transpose()
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wgs = wgs.transpose(0, 1, 4, 3, 2)
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print "Final shape:", np.shape(wgs)
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wgs = np.array(wgs.flatten(), dtype=np.float32)
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print "REPOSITIONED WEIGHTS:"
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print wgs
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bias = np.array(bias, dtype=np.float32)
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wgs.tofile(name + ".bin", format="f")
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bias.tofile(name + ".bias.bin", format="f")
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print "WEIGHTS saved\n"
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def get_session(gpu_fraction=0.5):
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gpu_options = tf.GPUOptions(allow_growth=True)
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#per_process_gpu_memory_fraction=gpu_fraction)
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return tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
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def export_bidir(name, weights):
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print ("######## EXPORT", name, "LAYER ########")
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for w in weights:
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print(np.shape(w))
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#https://github.com/fchollet/keras/wiki/Converting-convolution-kernels-from-Theano-to-TensorFlow-and-vice-versa
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if __name__ == '__main__':
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KTF.set_session(get_session())
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print("DATA FORMAT: ", keras.backend.image_data_format())
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parser = argparse.ArgumentParser(description='KERAS WEIGHTS EXPORTER TO CUDNN')
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parser.add_argument('model',type=str,
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@@ -103,31 +58,41 @@ if __name__ == '__main__':
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args = parser.parse_args()
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print "DATA FORMAT: ", keras.backend.image_data_format()
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print("DATA FORMAT: ", keras.backend.image_data_format())
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print "Load model: ", args.model
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print("Load model: ", args.model)
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model = load_model(args.model)
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model.summary()
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weights = model.get_weights()
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ws = np.shape(weights)
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print "Weights shape:", ws
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print("Weights shape:", ws)
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if not os.path.exists(args.output):
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os.makedirs(args.output)
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num = 0
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name_num = 0
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for l in model.layers:
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name = l.name
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if name.startswith("conv3d"):
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export_conv3d(args.output + "/conv" + str(name_num), weights[num], weights[num+1])
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elif name.startswith("conv2d"):
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export_conv2d(args.output + "/conv" + str(name_num), weights[num], weights[num+1])
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elif name.startswith("dense"):
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export_dense(args.output + "/dense" + str(name_num), weights[num], weights[num+1])
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else:
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print "skip:", name, "has no weights"
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continue
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name_num += 1
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num += 2
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print("\n\nNAME: ", l.name)
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print("input: ", l.input_shape, " output: ", l.output_shape)
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wgs = l.get_weights()
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print("wgs num: ", len(wgs))
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name = l.name
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if name.startswith("conv3d"):
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export_layer(args.output + "/" + name, wgs[0], wgs[1])
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elif name.startswith("conv2d"):
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export_layer(args.output + "/" + name, wgs[0], wgs[1])
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elif name.startswith("conv1d"):
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export_layer(args.output + "/" + name, wgs[0], wgs[1])
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elif name.startswith("dense"):
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export_layer(args.output + "/" + name, wgs[0], wgs[1])
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elif name.startswith("bidirectional"):
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export_bidir(args.output + "/" + name, wgs)
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else:
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print ("skip:", name, "has no weights")
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continue
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