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convert.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from models.ecbsr import ECBSR
from models.plainsr import PlainSR
from models.tf.plainsr import plainsr_tf
from torch.utils.data import DataLoader
import math
import argparse, yaml
import utils
import os
from tqdm import tqdm
import tensorflow.keras.layers as TF_Layers
from tensorflow.python.tools import freeze_graph
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
import tensorflow as tf
from tensorflow.compat.v1 import graph_util
from tensorflow.python.keras import backend as tf_keras_backend
parser = argparse.ArgumentParser(description='ECBSR convertor')
## yaml configuration files
parser.add_argument('--config', type=str, default=None, help = 'pre-config file for training')
## paramters for ecbsr
parser.add_argument('--scale', type=int, default=4, help = 'scale for sr network')
parser.add_argument('--colors', type=int, default=1, help = '1(Y channls of YCbCr), 3(RGB)')
parser.add_argument('--m_ecbsr', type=int, default=4, help = 'number of ecb')
parser.add_argument('--c_ecbsr', type=int, default=8, help = 'channels of ecb')
parser.add_argument('--idt_ecbsr', type=int, default=0, help = 'incorporate identity mapping in ecb or not')
parser.add_argument('--act_type', type=str, default='prelu', help = 'prelu, relu, splus, rrelu')
parser.add_argument('--pretrain', type=str, default=None, help = 'path of pretrained model')
parser.add_argument('--target_frontend', type=str, default='pb-ckpt', help = 'target front-end for inference engine, e.g. onnx/pb-ckpt/pb-1.x/pb-2.x/tflite-fp32')
parser.add_argument('--output_folder', type=str, default='./', help = 'output folder')
parser.add_argument('--is_dynamic_batches', type=int, default=0, help = 'dynamic batches or not')
parser.add_argument('--inp_n', type=int, default=1, help = 'batch size of input data')
parser.add_argument('--inp_c', type=int, default=1, help = 'channel size of input data')
parser.add_argument('--inp_h', type=int, default=270, help = 'height of input data')
parser.add_argument('--inp_w', type=int, default=480, help = 'width of input data')
if __name__ == '__main__':
args = parser.parse_args()
if args.config:
opt = vars(args)
yaml_args = yaml.load(open(args.config), Loader=yaml.FullLoader)
opt.update(yaml_args)
if not os.path.exists(args.output_folder):
os.makedirs(args.output_folder)
if args.target_frontend == 'pb-1.x':
# necessary !!!
tf.compat.v1.disable_eager_execution()
device = torch.device('cpu')
## definitions of model, loss, and optimizer
model_ecbsr = ECBSR(module_nums=args.m_ecbsr, channel_nums=args.c_ecbsr, with_idt=args.idt_ecbsr, act_type=args.act_type, scale=args.scale, colors=args.colors).to(device)
model_plain = PlainSR(module_nums=args.m_ecbsr, channel_nums=args.c_ecbsr, act_type=args.act_type, scale=args.scale, colors=args.colors).to(device)
if args.pretrain is not None:
print("load pretrained model: {}!".format(args.pretrain))
model_ecbsr.load_state_dict(torch.load(args.pretrain))
else:
raise ValueError('the pretrain path is invalud!')
## copy weights from ecbsr to plainsr
depth = len(model_ecbsr.backbone)
for d in range(depth):
module = model_ecbsr.backbone[d]
act_type = module.act_type
RK, RB = module.rep_params()
model_plain.backbone[d].conv3x3.weight.data = RK
model_plain.backbone[d].conv3x3.bias.data = RB
if act_type == 'relu': pass
elif act_type == 'linear': pass
elif act_type == 'prelu': model_plain.backbone[d].act.weight.data = module.act.weight.data
else: raise ValueError('invalid type of activation!')
## convert model to onnx
output_name = utils.cur_timestamp_str()
if args.target_frontend == 'onnx':
output_name = os.path.join(args.output_folder, output_name + '.onnx')
batch_size = args.inp_n
fake_x = torch.rand(batch_size, args.inp_c, args.inp_h, args.inp_w, requires_grad=False)
dynamic_params = None
if args.is_dynamic_batches:
dynamic_params = {'input': {0: 'batch_size'}, 'output': {0: 'batch_size'}}
torch.onnx.export(
model_plain,
fake_x,
output_name,
export_params=True,
opset_version=10,
do_constant_folding=True,
input_names=['input'],
output_names=['output'],
dynamic_axes=dynamic_params
)
elif args.target_frontend == 'pb-ckpt' or \
args.target_frontend == 'pb-1.x' or \
args.target_frontend == 'pb-2.x' or \
args.target_frontend == 'tflite-fp32':
# output_name = os.path.join(args.output_folder, output_name + '.pb')
tf_raw_dir = os.path.join(args.output_folder, output_name )
model_plain_tf = plainsr_tf(args.m_ecbsr, args.c_ecbsr, args.act_type, args.scale, args.colors, args.inp_h, args.inp_w)
depth = len(model_plain.backbone)
tf_idx = 0
for d in range(depth):
tf_idx += 1
module = model_plain.backbone[d]
act_type = module.act_type
## update weights of conv3x3
K, B = module.conv3x3.weight, module.conv3x3.bias
K, B = K.detach().numpy(), B.detach().numpy()
RK_tf, RB_tf = K.transpose([2, 3, 1, 0]), B
wgt_tf = [RK_tf, RB_tf]
model_plain_tf.layers[tf_idx].set_weights(wgt_tf)
## update weights of activation
if act_type == 'linear':
pass
elif act_type == 'relu':
tf_idx += 1
elif act_type == 'prelu':
tf_idx += 1
slope = module.act.weight.data
slope = slope.view((1,1,-1))
slope = slope.detach().numpy()
slope_tf = slope
wgt_tf = [slope_tf]
model_plain_tf.layers[tf_idx].set_weights(wgt_tf)
else:
raise ValueError('invalid type of activation!')
if args.target_frontend == 'pb-ckpt':
# save checkpoints
model_plain_tf.save(tf_raw_dir, overwrite=True, include_optimizer=False, save_format='tf')
if args.target_frontend == 'tflite-fp32':
# save checkpoints
model_plain_tf.save(tf_raw_dir, overwrite=True, include_optimizer=False, save_format='tf')
# # Load trained SavedModel
model = tf.saved_model.load(tf_raw_dir)
# Setup fixed input shape
input_shape = [1, args.inp_h, args.inp_w, args.inp_c]
concrete_func = model.signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
concrete_func.inputs[0].set_shape(input_shape)
# Get tf.lite converter instance
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
# Use full integer operations in quantized model
# converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_LATENCY]
converter.inference_input_type = tf.float32
converter.inference_output_type = tf.float32
tflite_model = converter.convert()
open('{}/model.tflite'.format(tf_raw_dir), 'wb').write(tflite_model)
elif args.target_frontend == 'pb-1.x':
# save pb, tensorflow-1.x
with tf_keras_backend.get_session() as sess:
output_names = [out.op.name for out in model_plain_tf.outputs]
input_graph_def = sess.graph.as_graph_def()
for node in input_graph_def.node:
node.device = ""
graph = graph_util.remove_training_nodes(input_graph_def)
graph_frozen = graph_util.convert_variables_to_constants(sess, graph, output_names)
tf.io.write_graph(
graph_or_graph_def=graph_frozen,
logdir=tf_raw_dir,
name='model.pb',
as_text=False
)
elif args.target_frontend == 'pb-2.x':
# Get frozen ConcreteFunction
full_model = tf.function(lambda x: model_plain_tf(x))
full_model = full_model.get_concrete_function([tf.TensorSpec(model_input.shape, model_input.dtype) for model_input in model_plain_tf.inputs])
frozen_func = convert_variables_to_constants_v2(full_model)
frozen_func.graph.as_graph_def()
tf.io.write_graph(
graph_or_graph_def=frozen_func.graph,
logdir=tf_raw_dir,
name="model.pb",
as_text=False
)
else:
raise ValueError('invalid type of frontend!')