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Add different padding supports in SR dnn native scripts for the paddi… … …ng method support in FFmpeg dnn_nativeSr dnn native #1

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Add different padding supports in SR dnn native scripts for the paddi… … …ng method support in FFmpeg dnn_nativeSr dnn native #1

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58422bc
Add different padding supports in SR dnn native scripts for the paddi…
mengxuewei May 7, 2019
0fa38ee
Add different padding supports in SR dnn native scripts for the paddi…
mengxuewei May 16, 2019
e0faa8a
Merge branch 'sr_dnn_native' of https://github.com/XueweiMeng/sr into…
mengxuewei May 16, 2019
f1a7a2d
Add denpendency libaray
mengxuewei May 20, 2019
59ba043
Add dilation option in generate
mengxuewei May 22, 2019
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38 changes: 22 additions & 16 deletions generate_header_and_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,12 +2,18 @@
import numpy as np
import argparse
import os
import enum
from models.model_espcn import ESPCN
from models.model_srcnn import SRCNN
from models.model_vespcn import VESPCN
from models.model_vsrnet import VSRnet
from collections import OrderedDict

@enum.unique
class Padding(enum.Enum):
Valid = 0
Same = 1
Same_clamp_to_edge = 2

def get_arguments():
parser = argparse.ArgumentParser(description='generate c header with model weights and binary model file')
Expand Down Expand Up @@ -64,9 +70,9 @@ def dump_to_file(file, values, name):
file.write('\n};\n')


def write_conv_layer(kernel, bias, activation, model_file):
def write_conv_layer(kernel, bias, dilation_rate, padding, activation, model_file):
kernel = np.transpose(kernel, [3, 0, 1, 2])
np.array([1, activation, kernel.shape[3], kernel.shape[0], kernel.shape[1]], dtype=np.uint32).tofile(model_file)
np.array([1, dilation_rate, padding.value, activation, kernel.shape[3], kernel.shape[0], kernel.shape[1]], dtype=np.uint32).tofile(model_file)
kernel.tofile(model_file)
bias.tofile(model_file)

Expand All @@ -77,34 +83,34 @@ def write_depth_to_space_layer(block_size, model_file):

def prepare_native_mf_srcnn(weights, model_file):
np.array([3], dtype=np.uint32).tofile(model_file)
write_conv_layer(weights['srcnn/conv1/kernel:0'], weights['srcnn/conv1/bias:0'], 0, model_file)
write_conv_layer(weights['srcnn/conv2/kernel:0'], weights['srcnn/conv2/bias:0'], 0, model_file)
write_conv_layer(weights['srcnn/conv3/kernel:0'], weights['srcnn/conv3/bias:0'], 0, model_file)
write_conv_layer(weights['srcnn/conv1/kernel:0'], weights['srcnn/conv1/bias:0'], 1, Padding.Same_clamp_to_edge, 0, model_file)
write_conv_layer(weights['srcnn/conv2/kernel:0'], weights['srcnn/conv2/bias:0'], 1, Padding.Same_clamp_to_edge, 0, model_file)
write_conv_layer(weights['srcnn/conv3/kernel:0'], weights['srcnn/conv3/bias:0'], 1, Padding.Same_clamp_to_edge, 0, model_file)


def prepare_native_mf_espcn(weights, model_file, scale_factor):
np.array([4], dtype=np.uint32).tofile(model_file)
write_conv_layer(weights['espcn/conv1/kernel:0'], weights['espcn/conv1/bias:0'], 1, model_file)
write_conv_layer(weights['espcn/conv2/kernel:0'], weights['espcn/conv2/bias:0'], 1, model_file)
write_conv_layer(weights['espcn/conv3/kernel:0'], weights['espcn/conv3/bias:0'], 2, model_file)
write_conv_layer(weights['espcn/conv1/kernel:0'], weights['espcn/conv1/bias:0'], 1, Padding.Same_clamp_to_edge, 1, model_file)
write_conv_layer(weights['espcn/conv2/kernel:0'], weights['espcn/conv2/bias:0'], 1, Padding.Same_clamp_to_edge, 1, model_file)
write_conv_layer(weights['espcn/conv3/kernel:0'], weights['espcn/conv3/bias:0'], 1, Padding.Same_clamp_to_edge, 2, model_file)
write_depth_to_space_layer(scale_factor, model_file)


def prepare_native_mf_vespcn(weights, model_file, scale_factor):
np.array([6], dtype=np.uint32).tofile(model_file)
write_conv_layer(weights['vespcn/conv1/kernel:0'], weights['vespcn/conv1/bias:0'], 0, model_file)
write_conv_layer(weights['vespcn/conv2/kernel:0'], weights['vespcn/conv2/bias:0'], 0, model_file)
write_conv_layer(weights['vespcn/conv3/kernel:0'], weights['vespcn/conv3/bias:0'], 0, model_file)
write_conv_layer(weights['vespcn/conv4/kernel:0'], weights['vespcn/conv4/bias:0'], 0, model_file)
write_conv_layer(weights['vespcn/conv5/kernel:0'], weights['vespcn/conv5/bias:0'], 0, model_file)
write_conv_layer(weights['vespcn/conv1/kernel:0'], weights['vespcn/conv1/bias:0'], 1, Padding.Same_clamp_to_edge, 0, model_file)
write_conv_layer(weights['vespcn/conv2/kernel:0'], weights['vespcn/conv2/bias:0'], 1, Padding.Same_clamp_to_edge, 0, model_file)
write_conv_layer(weights['vespcn/conv3/kernel:0'], weights['vespcn/conv3/bias:0'], 1, Padding.Same_clamp_to_edge, 0, model_file)
write_conv_layer(weights['vespcn/conv4/kernel:0'], weights['vespcn/conv4/bias:0'], 1, Padding.Same_clamp_to_edge, 0, model_file)
write_conv_layer(weights['vespcn/conv5/kernel:0'], weights['vespcn/conv5/bias:0'], 1, Padding.Same_clamp_to_edge, 0, model_file)
write_depth_to_space_layer(scale_factor, model_file)


def prepare_native_mf_vsrnet(weights, model_file):
np.array([3], dtype=np.uint32).tofile(model_file)
write_conv_layer(weights['vsrnet/conv1/kernel:0'], weights['vsrnet/conv1/bias:0'], 0, model_file)
write_conv_layer(weights['vsrnet/conv2/kernel:0'], weights['vsrnet/conv2/bias:0'], 0, model_file)
write_conv_layer(weights['vsrnet/conv3/kernel:0'], weights['vsrnet/conv3/bias:0'], 0, model_file)
write_conv_layer(weights['vsrnet/conv1/kernel:0'], weights['vsrnet/conv1/bias:0'], 1, Padding.Same_clamp_to_edge, 0, model_file)
write_conv_layer(weights['vsrnet/conv2/kernel:0'], weights['vsrnet/conv2/bias:0'], 1, Padding.Same_clamp_to_edge, 0, model_file)
write_conv_layer(weights['vsrnet/conv3/kernel:0'], weights['vsrnet/conv3/bias:0'], 1, Padding.Same_clamp_to_edge, 0, model_file)


def main():
Expand Down