def resnet_basic(input, out_feature_map_count, bn_time_const):
c1 = conv_bn_relu_layer(input, out_feature_map_count, [3, 3], [1, 1],
bn_time_const)
c2 = conv_bn_layer(c1, out_feature_map_count, [3, 3], [1, 1],
bn_time_const)
p = c2 + input
return relu(p)
def create_resnet_model(input, num_classes):
bn_time_const = 4096
c_map1 = 16
feat_scale = 0.00390625
input_norm = element_times(feat_scale, input)
conv = conv_bn_relu_layer(input_norm, c_map1, [3, 3], [1, 1],
bn_time_const)
r1_1 = resnet_basic_stack3(conv, c_map1, bn_time_const)
c_map2 = 32
r2_1 = resnet_basic_inc(r1_1, c_map2, [2, 2], bn_time_const)
r2_2 = resnet_basic_stack2(r2_1, c_map2, bn_time_const)
c_map3 = 64
r3_1 = resnet_basic_inc(r2_2, c_map3, [2, 2], bn_time_const)
r3_2 = resnet_basic_stack2(r3_1, c_map3, bn_time_const)
# Global average pooling
poolw = 8
poolh = 8
poolh_stride = 1
poolv_stride = 1
pool = pooling(r3_2, AVG_POOLING, (1, poolh, poolw),
(1, poolv_stride, poolh_stride))
return linear_layer(pool, num_classes)
def create_resnet_model(input, num_classes):
bn_time_const = 4096
c_map1 = 16
feat_scale = 0.00390625
input_norm = element_times(feat_scale, input)
conv = conv_bn_relu_layer(input, c_map1, [3, 3], [1, 1], bn_time_const)
r1_1 = resnet_basic_stack3(conv, c_map1, bn_time_const)
c_map2 = 32
r2_1 = resnet_basic_inc(r1_1, c_map2, [2, 2], bn_time_const)
r2_2 = resnet_basic_stack2(r2_1, c_map2, bn_time_const)
c_map3 = 64
r3_1 = resnet_basic_inc(r2_2, c_map3, [2, 2], bn_time_const)
r3_2 = resnet_basic_stack2(r3_1, c_map3, bn_time_const)
# Global average pooling
poolw = 8
poolh = 8
poolh_stride = 1
poolv_stride = 1
pool = pooling(r3_2, AVG_POOLING, (1, poolh, poolw), (1, poolv_stride, poolh_stride))
return linear_layer(pool, num_classes)
def resnet_classifer(input, num_classes):
conv_w_scale = 7.07
conv_b_value = 0
fc1_w_scale = 0.4
fc1_b_value = 0
sc_value = 1
bn_time_const = 4096
kernel_width = 3
kernel_height = 3
conv1_w_scale = 0.26
c_map1 = 16
conv1 = conv_bn_relu_layer(input, c_map1, kernel_width, kernel_height, 1,
1, conv1_w_scale, conv_b_value, sc_value,
bn_time_const)
rn1_1 = resnet_node2(conv1, c_map1, kernel_width, kernel_height,
conv1_w_scale, conv_b_value, sc_value, bn_time_const)
rn1_2 = resnet_node2(rn1_1, c_map1, kernel_width, kernel_height,
conv1_w_scale, conv_b_value, sc_value, bn_time_const)
rn1_3 = resnet_node2(rn1_2, c_map1, kernel_width, kernel_height,
conv1_w_scale, conv_b_value, sc_value, bn_time_const)
c_map2 = 32
rn2_1_wProj = get_projection_map(c_map2, c_map1)
rn2_1 = resnet_node2_inc(rn1_3, c_map2, kernel_width, kernel_height,
conv1_w_scale, conv_b_value, sc_value,
bn_time_const, rn2_1_wProj)
rn2_2 = resnet_node2(rn2_1, c_map2, kernel_width, kernel_height,
conv1_w_scale, conv_b_value, sc_value, bn_time_const)
rn2_3 = resnet_node2(rn2_2, c_map2, kernel_width, kernel_height,
conv1_w_scale, conv_b_value, sc_value, bn_time_const)
c_map3 = 64
rn3_1_wProj = get_projection_map(c_map3, c_map2)
rn3_1 = resnet_node2_inc(rn2_3, c_map3, kernel_width, kernel_height,
conv1_w_scale, conv_b_value, sc_value,
bn_time_const, rn3_1_wProj)
rn3_2 = resnet_node2(rn3_1, c_map3, kernel_width, kernel_height,
conv1_w_scale, conv_b_value, sc_value, bn_time_const)
rn3_3 = resnet_node2(rn3_2, c_map3, kernel_width, kernel_height,
conv1_w_scale, conv_b_value, sc_value, bn_time_const)
# Global average pooling
poolw = 8
poolh = 8
poolh_stride = 1
poolv_stride = 1
pool = pooling(rn3_3, AVG_POOLING, (1, poolh, poolw),
(1, poolv_stride, poolh_stride))
out_times_params = parameter(shape=(c_map3, 1, 1, num_classes),
init=glorot_uniform())
out_bias_params = parameter(shape=(num_classes), init=0)
t = times(pool, out_times_params)
return t + out_bias_params
def resnet_classifer(input, num_classes):
conv_w_scale = 7.07
conv_b_value = 0
fc1_w_scale = 0.4
fc1_b_value = 0
sc_value = 1
bn_time_const = 4096
kernel_width = 3
kernel_height = 3
conv1_w_scale = 0.26
c_map1 = 16
conv1 = conv_bn_relu_layer(input, c_map1, kernel_width, kernel_height,
1, 1, conv1_w_scale, conv_b_value, sc_value, bn_time_const)
rn1_1 = resnet_node2(conv1, c_map1, kernel_width, kernel_height,
conv1_w_scale, conv_b_value, sc_value, bn_time_const)
rn1_2 = resnet_node2(rn1_1, c_map1, kernel_width, kernel_height,
conv1_w_scale, conv_b_value, sc_value, bn_time_const)
rn1_3 = resnet_node2(rn1_2, c_map1, kernel_width, kernel_height,
conv1_w_scale, conv_b_value, sc_value, bn_time_const)
c_map2 = 32
rn2_1_wProj = get_projection_map(c_map2, c_map1)
rn2_1 = resnet_node2_inc(rn1_3, c_map2, kernel_width, kernel_height,
conv1_w_scale, conv_b_value, sc_value, bn_time_const, rn2_1_wProj)
rn2_2 = resnet_node2(rn2_1, c_map2, kernel_width, kernel_height,
conv1_w_scale, conv_b_value, sc_value, bn_time_const)
rn2_3 = resnet_node2(rn2_2, c_map2, kernel_width, kernel_height,
conv1_w_scale, conv_b_value, sc_value, bn_time_const)
c_map3 = 64
rn3_1_wProj = get_projection_map(c_map3, c_map2)
rn3_1 = resnet_node2_inc(rn2_3, c_map3, kernel_width, kernel_height,
conv1_w_scale, conv_b_value, sc_value, bn_time_const, rn3_1_wProj)
rn3_2 = resnet_node2(rn3_1, c_map3, kernel_width, kernel_height,
conv1_w_scale, conv_b_value, sc_value, bn_time_const)
rn3_3 = resnet_node2(rn3_2, c_map3, kernel_width, kernel_height,
conv1_w_scale, conv_b_value, sc_value, bn_time_const)
# Global average pooling
poolw = 8
poolh = 8
poolh_stride = 1
poolv_stride = 1
pool = pooling(rn3_3, AVG_POOLING, (1, poolh, poolw), (1, poolv_stride, poolh_stride))
out_times_params = parameter(shape=(c_map3, 1, 1, num_classes), init=glorot_uniform())
out_bias_params = parameter(shape=(num_classes), init=0)
t = times(pool, out_times_params)
return t + out_bias_params
def resnet_basic_inc(input, out_feature_map_count, strides, bn_time_const):
c1 = conv_bn_relu_layer(input, out_feature_map_count, [3, 3], strides, bn_time_const)
c2 = conv_bn_layer(c1, out_feature_map_count, [3, 3], [1, 1], bn_time_const)
s = conv_bn_layer(input, out_feature_map_count, [1, 1], strides, bn_time_const)
p = c2 + s
return relu(p)
