import xflow
graph = xflow.new_graph()
input = graph.new_input(dims=(1,512,28,28))
input = graph.maxpool2d(input=input, kernels=(1,1), strides=(1,1), padding="SAME")
# Printing the performance of different multi-branch convolutions
graph.print_measurements()
i = 1
while i <= 32:
print("Num. Convs Per Grop = {}".format(i))
weight = graph.new_weight(dims=(512,512//i,3,3))
t = graph.conv2d(input=input,weight=weight,strides=(1,1),padding="SAME", activation="RELU")
i *= 2
#weight1 = graph.new_weight(dims=(256,8,3,3))
#t1 = graph.conv2d(input=input,weight=weight1,strides=(1,1), padding="SAME", activation="RELU")
#weight2 = graph.new_weight(dims=(256,16,3,3))
#t2 = graph.conv2d(input=input,weight=weight2,strides=(1,1), padding="SAME", activation="RELU")
#weight3 = graph.new_weight(dims=(256,32,3,3))
#t3 = graph.conv2d(input=input,weight=weight3,strides=(1,1), padding="SAME", activation="RELU")
#weight4 = graph.new_weight(dims=(256,64,3,3))
#t4 = graph.conv2d(input=input,weight=weight4,strides=(1,1), padding="SAME", activation="RELU")
#weight5 = graph.new_weight(dims=(256,128,3,3))
#t5 = graph.conv2d(input=input,weight=weight5,strides=(1,1), padding="SAME", activation="RELU")
#weight6 = graph.new_weight(dims=(256,256,3,3))
#t6 = graph.conv2d(input=input,weight=weight6,strides=(1,1), padding="SAME", activation="RELU")
activation="RELU")
w2 = graph.new_weight(dims=(out_channels,t.dim(1),3,3))
t = graph.conv2d(input=t, weight=w2,
strides=strides, padding="SAME",
activation="RELU")
w3 = graph.new_weight(dims=(4*out_channels,t.dim(1),1,1))
t = graph.conv2d(input=t, weight=w3,
strides=(1,1), padding="SAME")
if (strides[0]>1) or (input.dim(1) != out_channels*4):
w4 = graph.new_weight(dims=(out_channels*4,input.dim(1),1,1))
input=graph.conv2d(input=input, weight=w4,
strides=strides, padding="SAME",
activation="RELU")
return graph.relu(graph.add(input, t))
graph = xf.new_graph()
input = graph.new_input(dims=(1,64,56,56))
t = input
for i in range(3):
t = resnet_block(graph, t, (1,1), 64)
strides = (2,2)
for i in range(4):
t = resnet_block(graph, t, strides, 128)
strides = (1,1)
strides = (2,2)
for i in range(6):
t = resnet_block(graph, t, strides, 256)
strides = (1,1)
strides = (2,2)
for i in range(3):
t = resnet_block(graph, t, strides, 512)