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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# File: vgg16.py
import argparse
import os
import sys
import tensorflow as tf
from tensorpack import *
from tensorpack.tfutils import argscope
from tensorpack.tfutils.summary import *
from tensorpack.utils.gpu import get_num_gpu
sys.path.append('../..')
from mpusim_conv2d.mpusim_conv2d_gradient import *
from mpusim_conv2d.mpusim_conv2d import *
from mpusim_fc.mpusim_mat_mul_gradient import *
from mpusim_fc.mpusim_fully_connected import *
from models.imagenet_utils import ImageNetModel, fbresnet_augmentor, get_imagenet_dataflow
session_conf = tf.ConfigProto(
intra_op_parallelism_threads=1,
inter_op_parallelism_threads=1)
sess = tf.Session(config=session_conf)
def convnormrelu(x, name, chan):
x = mpusim_conv2d(name, x, chan, 3)
x = BatchNorm(name + '_bn', x)
x = tf.nn.relu(x, name=name + '_relu')
return x
class Model(ImageNetModel):
def __init__(self,
data_format='NHWC',
wd=5e-4,
learning_rate=0.1,
activations_datatype_size_byte=1,
weights_datatype_size_byte=1,
results_datatype_size_byte=4,
systolic_array_height=256,
systolic_array_width=256,
accumulator_array_height=4096,
mpusim_logdir=''):
super(Model, self).__init__(data_format, wd)
self.activations_datatype_size_byte=activations_datatype_size_byte
self.weights_datatype_size_byte=weights_datatype_size_byte
self.results_datatype_size_byte=results_datatype_size_byte
self.systolic_array_height=systolic_array_height
self.systolic_array_width=systolic_array_width
self.accumulator_array_height=accumulator_array_height
self.mpusim_logdir=mpusim_logdir
def get_logits(self, image):
constant_init = tf.constant_initializer(1)
with argscope([mpusim_conv2d, MaxPooling, BatchNorm], data_format='NHWC'), \
argscope([mpusim_conv2d, mpusim_fully_connected],
kernel_initializer=constant_init,
activations_datatype_size_byte=self.activations_datatype_size_byte,
weights_datatype_size_byte=self.weights_datatype_size_byte,
results_datatype_size_byte=self.results_datatype_size_byte,
systolic_array_height=self.systolic_array_height,
systolic_array_width=self.systolic_array_width,
activation_fifo_depth=8,
accumulator_array_height=self.accumulator_array_height,
log_file_output_dir=self.mpusim_logdir,
model_name='vgg16_sys_arr_h_{}_sys_arr_w_{}_acc_arr_h_{}'.format(self.systolic_array_height,
self.systolic_array_width,
self.accumulator_array_height)):
logits = (LinearWrap(image)
.apply(convnormrelu, 'conv1_1', 64)
.apply(convnormrelu, 'conv1_2', 64)
.MaxPooling('pool1', 2)
# 112
.apply(convnormrelu, 'conv2_1', 128)
.apply(convnormrelu, 'conv2_2', 128)
.MaxPooling('pool2', 2)
# 56
.apply(convnormrelu, 'conv3_1', 256)
.apply(convnormrelu, 'conv3_2', 256)
.apply(convnormrelu, 'conv3_3', 256)
.MaxPooling('pool3', 2)
# 28
.apply(convnormrelu, 'conv4_1', 512)
.apply(convnormrelu, 'conv4_2', 512)
.apply(convnormrelu, 'conv4_3', 512)
.MaxPooling('pool4', 2)
# 14
.apply(convnormrelu, 'conv5_1', 512)
.apply(convnormrelu, 'conv5_2', 512)
.apply(convnormrelu, 'conv5_3', 512)
.MaxPooling('pool5', 2)
## 7
.mpusim_fully_connected('fc6', 4096)
.tf.nn.relu(name='fc6_relu')
.mpusim_fully_connected('fc7', 4096)
.tf.nn.relu(name='fc7_relu')
.mpusim_fully_connected('fc8', 1000)())
add_param_summary(('.*', ['histogram', 'rms']))
return logits
def get_config(activations_datatype_size_byte,
weights_datatype_size_byte,
results_datatype_size_byte,
systolic_array_height,
systolic_array_width,
accumulator_array_height,
mpusim_logdir):
nr_tower = max(get_num_gpu(), 1)
BASE_LR = 0.01 * (1. / 256.)
logger.info("Running on {} towers. Batch size per tower: {}".format(nr_tower, 1))
infs = [ClassificationError('wrong-top1', 'val-error-top1'),
ClassificationError('wrong-top5', 'val-error-top5')]
data = QueueInput(FakeData(
[[1, 224, 224, 3], [1]], 1000, random=False, dtype='uint8'))
callbacks = []
return TrainConfig(
model=Model(
activations_datatype_size_byte=activations_datatype_size_byte,
weights_datatype_size_byte=weights_datatype_size_byte,
results_datatype_size_byte=results_datatype_size_byte,
systolic_array_height=systolic_array_height,
systolic_array_width=systolic_array_width,
accumulator_array_height=accumulator_array_height,
mpusim_logdir=mpusim_logdir),
data=data,
callbacks=callbacks,
steps_per_epoch=1,
max_epoch=1)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--activations-datatype-size-byte',
help='activations datatype size in byte',
type=int, default=1)
parser.add_argument('--weights-datatype-size-byte',
help='weights datatype size in byte',
type=int, default=1)
parser.add_argument('--results-datatype-size-byte',
help='results datatype size in byte',
type=int, default=4)
parser.add_argument('--systolic-array-height',
help='systolic array height',
type=int, default=256)
parser.add_argument('--systolic-array-width',
help='systolic array width',
type=int, default=256)
parser.add_argument('--accumulator-array-height',
help='accumulator array height',
type=int, default=4096)
parser.add_argument('--tensorpack-logdir-id', help='TensorPack training log directory id',
type=str, default='')
parser.add_argument('--mpusim-logdir', help='MPU simulator log directory',
type=str, default='.')
args = parser.parse_args()
logger.set_logger_dir(os.path.join('train_log', 'vgg16' + args.tensorpack_logdir_id))
config = get_config(args.activations_datatype_size_byte,
args.weights_datatype_size_byte,
args.results_datatype_size_byte,
args.systolic_array_height,
args.systolic_array_width,
args.accumulator_array_height,
args.mpusim_logdir)
launch_train_with_config(config, SimpleTrainer())