def stage_shuffle(input_data, stage, repeat_num, groups):
avgpool_layer = AVGPooling(3, 3, 2, 1)
residual = avgpool_layer.forward(input_data)
#savetxt('./dump/' + 'avg_pool.txt', residual)
w = np.load(stage + '0.g_conv_1x1_compress.conv1x1.weight.npy')
b = np.load(stage + '0.g_conv_1x1_compress.conv1x1.bias.npy')
if 'Stage2' in stage:
conv_layer = Convolution(w, b, stride=1, pad=0)
else:
conv_layer = GroupConvolution(w, b, stride=1, pad=0, groups=groups)
out = conv_layer.forward(input_data)
out_N, out_C, out_H, out_W = out.shape
gamma = np.load(stage +
'0.g_conv_1x1_compress.batch_norm.weight.npy').reshape(
(-1, 1))
beta = np.load(stage +
'0.g_conv_1x1_compress.batch_norm.bias.npy').reshape(
(-1, 1))
mean = np.load(
stage + '0.g_conv_1x1_compress.batch_norm.running_mean.npy').reshape(
(-1, 1))
var = np.load(stage +
'0.g_conv_1x1_compress.batch_norm.running_var.npy').reshape(
(-1, 1))
bn_layer = BatchNormalization(gamma,
beta,
running_mean=mean,
running_var=var)
out = bn_layer.forward(out.reshape(out_C, -1), train_flg=False)
relu_layer = Relu()
out = relu_layer.forward(out).reshape(out_N, out_C, out_H, out_W)
#savetxt('./dump/' + '1x1_comp.txt', out)
out = channel_shuffle(out, groups)
#savetxt('./dump/' + 'channel_shuffle.txt', out)
w = np.load(stage + '0.depthwise_conv3x3.weight.npy').transpose(1, 0, 2, 3)
b = np.load(stage + '0.depthwise_conv3x3.bias.npy')
dwconv_layer = DWConvolution(w, b, stride=2, pad=1)
out = dwconv_layer.forward(out)
#savetxt('./dump/' + 'dwconv.txt', out)
gamma = np.load(stage + '0.bn_after_depthwise.weight.npy').reshape((-1, 1))
beta = np.load(stage + '0.bn_after_depthwise.bias.npy').reshape((-1, 1))
mean = np.load(stage + '0.bn_after_depthwise.running_mean.npy').reshape(
(-1, 1))
var = np.load(stage + '0.bn_after_depthwise.running_var.npy').reshape(
(-1, 1))
bn_layer = BatchNormalization(gamma,
beta,
running_mean=mean,
running_var=var)
out_N, out_C, out_H, out_W = out.shape
out = bn_layer.forward(out.reshape(out_C, -1),
train_flg=False).reshape(out_N, out_C, out_H, out_W)
#savetxt('./dump/' + 'after_bn.txt', out)
w = np.load(stage + '0.g_conv_1x1_expand.conv1x1.weight.npy')
b = np.load(stage + '0.g_conv_1x1_expand.conv1x1.bias.npy')
groupconv_layer = GroupConvolution(w, b, stride=1, pad=0, groups=groups)
out = groupconv_layer.forward(out)
gamma = np.load(stage +
'0.g_conv_1x1_expand.batch_norm.weight.npy').reshape(
(-1, 1))
beta = np.load(stage + '0.g_conv_1x1_expand.batch_norm.bias.npy').reshape(
(-1, 1))
mean = np.load(stage +
'0.g_conv_1x1_expand.batch_norm.running_mean.npy').reshape(
(-1, 1))
var = np.load(stage +
'0.g_conv_1x1_expand.batch_norm.running_var.npy').reshape(
(-1, 1))
bn_layer = BatchNormalization(gamma,
beta,
running_mean=mean,
running_var=var)
out_N, out_C, out_H, out_W = out.shape
out = bn_layer.forward(out.reshape(out_C, -1),
train_flg=False).reshape(out_N, out_C, out_H, out_W)
#savetxt('./dump/' + 'gconv.txt', out)
out = np.concatenate((residual, out), 1)
#savetxt('./dump/' + 'combine.txt', out)
relu_layer = Relu()
out_N, out_C, out_H, out_W = out.shape
out = relu_layer.forward(out).reshape(out_N, out_C, out_H, out_W)
#savetxt('./dump/' + 'stage2.txt', out)
for i in range(1, repeat_num + 1):
residual = out
w = np.load(stage + str(i) + '.g_conv_1x1_compress.conv1x1.weight.npy')
b = np.load(stage + str(i) + '.g_conv_1x1_compress.conv1x1.bias.npy')
groupconv_layer = GroupConvolution(w,
b,
stride=1,
pad=0,
groups=groups)
out = groupconv_layer.forward(out)
out_N, out_C, out_H, out_W = out.shape
gamma = np.load(stage + str(i) +
'.g_conv_1x1_compress.batch_norm.weight.npy').reshape(
(-1, 1))
beta = np.load(stage + str(i) +
'.g_conv_1x1_compress.batch_norm.bias.npy').reshape(
(-1, 1))
mean = np.load(
stage + str(i) +
'.g_conv_1x1_compress.batch_norm.running_mean.npy').reshape(
(-1, 1))
var = np.load(
stage + str(i) +
'.g_conv_1x1_compress.batch_norm.running_var.npy').reshape((-1, 1))
bn_layer = BatchNormalization(gamma,
beta,
running_mean=mean,
running_var=var)
out = bn_layer.forward(out.reshape(out_C, -1), train_flg=False)
relu_layer = Relu()
out = relu_layer.forward(out).reshape(out_N, out_C, out_H, out_W)
#savetxt('./dump/' + str(i) + '_1x1_comp.txt', out)
out = channel_shuffle(out, groups)
#savetxt('./dump/' + 'channel_shuffle.txt', out)
w = np.load(stage + str(i) +
'.depthwise_conv3x3.weight.npy').transpose(1, 0, 2, 3)
b = np.load(stage + str(i) + '.depthwise_conv3x3.bias.npy')
dwconv_layer = DWConvolution(w, b, stride=1, pad=1)
out = dwconv_layer.forward(out)
#savetxt('./dump/' + 'dwconv.txt', out)
gamma = np.load(stage + str(i) +
'.bn_after_depthwise.weight.npy').reshape((-1, 1))
beta = np.load(stage + str(i) +
'.bn_after_depthwise.bias.npy').reshape((-1, 1))
mean = np.load(stage + str(i) +
'.bn_after_depthwise.running_mean.npy').reshape((-1, 1))
var = np.load(stage + str(i) +
'.bn_after_depthwise.running_var.npy').reshape((-1, 1))
bn_layer = BatchNormalization(gamma,
beta,
running_mean=mean,
running_var=var)
out_N, out_C, out_H, out_W = out.shape
out = bn_layer.forward(out.reshape(out_C, -1),
train_flg=False).reshape(
out_N, out_C, out_H, out_W)
#savetxt('./dump/' + 'after_bn.txt', out)
w = np.load(stage + str(i) + '.g_conv_1x1_expand.conv1x1.weight.npy')
b = np.load(stage + str(i) + '.g_conv_1x1_expand.conv1x1.bias.npy')
groupconv_layer = GroupConvolution(w,
b,
stride=1,
pad=0,
groups=groups)
out = groupconv_layer.forward(out)
gamma = np.load(stage + str(i) +
'.g_conv_1x1_expand.batch_norm.weight.npy').reshape(
(-1, 1))
beta = np.load(stage + str(i) +
'.g_conv_1x1_expand.batch_norm.bias.npy').reshape(
(-1, 1))
mean = np.load(
stage + str(i) +
'.g_conv_1x1_expand.batch_norm.running_mean.npy').reshape((-1, 1))
var = np.load(stage + str(i) +
'.g_conv_1x1_expand.batch_norm.running_var.npy').reshape(
(-1, 1))
bn_layer = BatchNormalization(gamma,
beta,
running_mean=mean,
running_var=var)
out_N, out_C, out_H, out_W = out.shape
out = bn_layer.forward(out.reshape(out_C, -1),
train_flg=False).reshape(
out_N, out_C, out_H, out_W)
#savetxt('./dump/' + 'gconv.txt', out)
out = np.add(residual, out)
#savetxt('./dump/' + str(i) + '_combine.txt', out)
relu_layer = Relu()
out_N, out_C, out_H, out_W = out.shape
out = relu_layer.forward(out).reshape(out_N, out_C, out_H, out_W)
#savetxt('./dump/' + str(i) + '_stage.txt', out)
return out
# coding: utf-8
import numpy as np
import sys
sys.path.append('../../')
from common.layers import Relu
relu = Relu()
#---------------------------------------
# forward
x = np.array([[1.0, -0.5], [-2.0, 3.0]])
print(x)
y = relu.forward(x)
print(y)
#---------------------------------------
# backward
dy = np.array([[5, 5], [5, 5]])
dx = relu.backward(dy)
print(dx)