added latency tests

tests/latency.py

@@ -0,0 +1,76 @@
+import tensorflow as tf
+from tensorflow import nn
+import tensorflow.keras as keras
+from tensorflow.keras import layers
+from tensorflow.keras.models import Model, Sequential
+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)
+
+  for i in range(n):
+    start = time.time()
+    nn.convolution(input, filter)
+    elapsed_time = time.time() - start
+    print("CPU Conv2D OP: {:.6f} s".format(elapsed_time))
+
+  for i in range(n):
+    start = time.time()
+    load_op.op_lib.MyConv2D_1(input=input, filter=filter)
+    elapsed_time = time.time() - start
+    print("FPGA Conv2D OP: {:.6f} s".format(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), 1000)
+
+def runLayer(inputShape, n):
+
+  input_x = tf.random.uniform(shape=inputShape)
+  input_y = tf.image.resize(input_x, (224,224))
+  
+  model = Sequential()
+  model.add(layers.Conv2D(
+    1,
+    kernel_size=5,
+    strides=(1, 1),
+    padding='valid',
+    data_format='channels_last',
+    dilation_rate=(1, 1),
+    activation=None,
+    use_bias=False,
+    trainable=True
+  ))
+  model.compile(loss=keras.losses.categorical_crossentropy,
+              optimizer=keras.optimizers.Adadelta())
+
+  for i in range(n):
+    start = time.time()
+    model.evaluate(input_x, input_y, verbose=0)
+    elapsed_time = time.time() - start
+    print("CPU Conv2D layer: {:.6f} s".format(elapsed_time))
+
+  model = Sequential()
+  model.add(Conv2DFPGA(1))
+  model.compile(loss=keras.losses.categorical_crossentropy,
+              optimizer=keras.optimizers.Adadelta())
+
+  for i in range(n):
+    start = time.time()
+    model.evaluate(input_x, input_y, verbose=0)
+    elapsed_time = time.time() - start
+    print("FPGA Conv2D layer: {:.6f} s".format(elapsed_time))
+
+runLayer((1,228,228,1), 1000)