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- import tensorflow as tf
- from tensorflow import nn
- import numpy as np
- import time
- import sys
- sys.path.append('../hostLib/')
- from hostLib.layers.conv2d import Conv2D as Conv2DFPGA
- from hostLib import load_op
- def run(inputShape, filterShape, n):
- input = tf.random.uniform(shape=inputShape)
- filter = tf.random.uniform(shape=filterShape)
- start = time.time()
- for i in range(n):
- nn.convolution(input, filter)
- elapsed_time = time.time() - start
- print("shapes: {:22s} {:22s}, count: {:6d}, CPU Conv2D OP time: {:.6f} s".format(str(inputShape), str(filterShape), n, elapsed_time))
- start = time.time()
- for i in range(n):
- load_op.op_lib.MyConv2D_1(input=input, filter=filter)
- elapsed_time = time.time() - start
- print("shapes: {:22s} {:22s}, count: {:6d}, FPGA Conv2D OP time: {:.6f} s".format(str(inputShape), str(filterShape), n, elapsed_time))
- input = tf.random.uniform(shape=[1,228,228,1])
- filter = tf.random.uniform(shape=[5,5,1,1])
- nn.convolution(input, filter)
- load_op.op_lib.MyConv2D_1(input=input, filter=filter)
- run((1,228,228,1), (5,5,1,1), 10000)
- run((10,228,228,1), (5,5,1,1), 1000)
- run((100,228,228,1), (5,5,1,1), 100)
- run((1000,228,228,1), (5,5,1,1), 10)
- run((1,228,228,1), (5,5,1,1), 10000)
- run((1,228,228,10), (5,5,10,1), 1000)
- run((1,228,228,100), (5,5,100,1), 100)
- run((1,228,228,1000), (5,5,1000,1), 10)
- run((1,228,228,1), (5,5,1,1), 10000)
- run((1,228,228,1), (5,5,1,10), 1000)
- run((1,228,228,1), (5,5,1,100), 100)
- run((1,228,228,1), (5,5,1,1000), 10)
- run((1,228,228,1), (5,5,1,1000), 10)
- run((1,228,228,10), (5,5,10,100), 10)
- run((1,228,228,100), (5,5,100,10), 10)
- run((1,228,228,1000), (5,5,1000,1), 10)
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