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convert.py
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convert.py
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#!/usr/bin/env python
# coding: utf-8
#
# Author: Kazuto Nakashima
# URL: http://kazuto1011.github.io
# Created: 2017-11-15
from __future__ import absolute_import, division, print_function
import re
import traceback
from collections import Counter, OrderedDict
import click
import numpy as np
import torch
from addict import Dict
from libs import caffe_pb2
from libs.models import DeepLabV1_ResNet101, DeepLabV2_ResNet101_MSC
def parse_caffemodel(model_path):
caffemodel = caffe_pb2.NetParameter()
with open(model_path, "rb") as f:
caffemodel.MergeFromString(f.read())
# Check trainable layers
print(
*Counter(
[(layer.type, len(layer.blobs)) for layer in caffemodel.layer]
).most_common(),
sep="\n",
)
params = OrderedDict()
previous_layer_type = None
for layer in caffemodel.layer:
# Skip the shared branch
if "res075" in layer.name or "res05" in layer.name:
continue
print(
"\033[34m[Caffe]\033[00m",
"{} ({}): {}".format(layer.name, layer.type, len(layer.blobs)),
)
# Convolution or Dilated Convolution
if "Convolution" in layer.type:
params[layer.name] = {}
params[layer.name]["kernel_size"] = layer.convolution_param.kernel_size[0]
params[layer.name]["weight"] = list(layer.blobs[0].data)
if len(layer.blobs) == 2:
params[layer.name]["bias"] = list(layer.blobs[1].data)
if len(layer.convolution_param.stride) == 1: # or []
params[layer.name]["stride"] = layer.convolution_param.stride[0]
else:
params[layer.name]["stride"] = 1
if len(layer.convolution_param.pad) == 1: # or []
params[layer.name]["padding"] = layer.convolution_param.pad[0]
else:
params[layer.name]["padding"] = 0
if isinstance(layer.convolution_param.dilation, int):
params[layer.name]["dilation"] = layer.convolution_param.dilation
elif len(layer.convolution_param.dilation) == 1:
params[layer.name]["dilation"] = layer.convolution_param.dilation[0]
else:
params[layer.name]["dilation"] = 1
# Fully-connected
elif "InnerProduct" in layer.type:
params[layer.name] = {}
params[layer.name]["weight"] = list(layer.blobs[0].data)
if len(layer.blobs) == 2:
params[layer.name]["bias"] = list(layer.blobs[1].data)
# Batch Normalization
elif "BatchNorm" in layer.type:
params[layer.name] = {}
params[layer.name]["running_mean"] = (
np.array(layer.blobs[0].data) / layer.blobs[2].data[0]
)
params[layer.name]["running_var"] = (
np.array(layer.blobs[1].data) / layer.blobs[2].data[0]
)
params[layer.name]["eps"] = layer.batch_norm_param.eps
params[layer.name]["momentum"] = (
1 - layer.batch_norm_param.moving_average_fraction
)
params[layer.name]["num_batches_tracked"] = np.array(0)
batch_norm_layer = layer.name
# Scale
elif "Scale" in layer.type:
assert previous_layer_type == "BatchNorm"
params[batch_norm_layer]["weight"] = list(layer.blobs[0].data)
params[batch_norm_layer]["bias"] = list(layer.blobs[1].data)
elif "Pooling" in layer.type:
params[layer.name] = {}
params[layer.name]["kernel_size"] = layer.pooling_param.kernel_size
params[layer.name]["stride"] = layer.pooling_param.stride
params[layer.name]["padding"] = layer.pooling_param.pad
previous_layer_type = layer.type
return params
# Hard coded translater
def translate_layer_name(source, target="base"):
def layer_block_branch(source, target):
target += "layer{}".format(source[0][0])
if len(source[0][1:]) == 1:
block = {"a": 1, "b": 2, "c": 3}.get(source[0][1:])
else:
block = int(source[0][2:]) + 1
target += ".block{}".format(block)
branch = source[1][6:]
if branch == "1":
target += ".shortcut"
elif branch == "2a":
target += ".reduce"
elif branch == "2b":
target += ".conv3x3"
elif branch == "2c":
target += ".increase"
return target
source = source.split("_")
if "pool" in source[0]:
target += "layer1.pool"
elif "fc" in source[0]:
if len(source) == 3:
stage = source[2]
target += "aspp.{}".format(stage)
else:
target += "fc"
elif "conv1" in source[0]:
target += "layer1.conv1.conv"
elif "conv1" in source[1]:
target += "layer1.conv1.bn"
elif "res" in source[0]:
source[0] = source[0].replace("res", "")
target = layer_block_branch(source, target)
target += ".conv"
elif "bn" in source[0]:
source[0] = source[0].replace("bn", "")
target = layer_block_branch(source, target)
target += ".bn"
return target
@click.command()
@click.option(
"-d",
"--dataset",
type=click.Choice(["voc12", "coco"]),
required=True,
help="Caffemodel",
)
def main(dataset):
"""
Convert caffemodels to pytorch models
"""
WHITELIST = ["kernel_size", "stride", "padding", "dilation", "eps", "momentum"]
CONFIG = Dict(
{
"voc12": {
# For loading the provided VOC 2012 caffemodel
"PATH_CAFFE_MODEL": "data/models/voc12/deeplabv2_resnet101_msc/caffemodel/train2_iter_20000.caffemodel",
"PATH_PYTORCH_MODEL": "data/models/voc12/deeplabv2_resnet101_msc/caffemodel/deeplabv2_resnet101_msc-vocaug.pth",
"N_CLASSES": 21,
"MODEL": "DeepLabV2_ResNet101_MSC",
"HEAD": "base.",
},
"coco": {
# For loading the provided initial weights pre-trained on COCO
"PATH_CAFFE_MODEL": "data/models/coco/deeplabv1_resnet101/caffemodel/init.caffemodel",
"PATH_PYTORCH_MODEL": "data/models/coco/deeplabv1_resnet101/caffemodel/deeplabv1_resnet101-coco.pth",
"N_CLASSES": 91,
"MODEL": "DeepLabV1_ResNet101",
"HEAD": "",
},
}.get(dataset)
)
params = parse_caffemodel(CONFIG.PATH_CAFFE_MODEL)
model = eval(CONFIG.MODEL)(n_classes=CONFIG.N_CLASSES)
model.eval()
reference_state_dict = model.state_dict()
rel_tol = 1e-7
converted_state_dict = OrderedDict()
for caffe_layer, caffe_layer_dict in params.items():
for param_name, caffe_values in caffe_layer_dict.items():
pytorch_layer = translate_layer_name(caffe_layer, CONFIG.HEAD)
if pytorch_layer:
pytorch_param = pytorch_layer + "." + param_name
# Parameter check
if param_name in WHITELIST:
pytorch_values = eval("model." + pytorch_param)
if isinstance(pytorch_values, tuple):
assert (
pytorch_values[0] == caffe_values
), "Inconsistent values: {} @{} (Caffe), {} @{} (PyTorch)".format(
caffe_values,
caffe_layer + "/" + param_name,
pytorch_values,
pytorch_param,
)
else:
assert (
abs(pytorch_values - caffe_values) < rel_tol
), "Inconsistent values: {} @{} (Caffe), {} @{} (PyTorch)".format(
caffe_values,
caffe_layer + "/" + param_name,
pytorch_values,
pytorch_param,
)
print(
"\033[34m[Passed!]\033[00m",
(caffe_layer + "/" + param_name).ljust(35),
"->",
pytorch_param,
)
continue
# Weight conversion
if pytorch_param in reference_state_dict:
caffe_values = torch.tensor(caffe_values)
caffe_values = caffe_values.view_as(
reference_state_dict[pytorch_param]
)
converted_state_dict[pytorch_param] = caffe_values
print(
"\033[32m[Copied!]\033[00m",
(caffe_layer + "/" + param_name).ljust(35),
"->",
pytorch_param,
)
print("\033[32mVerify the converted model\033[00m")
model.load_state_dict(converted_state_dict)
print('Saving to "{}"'.format(CONFIG.PATH_PYTORCH_MODEL))
torch.save(converted_state_dict, CONFIG.PATH_PYTORCH_MODEL)
if __name__ == "__main__":
main()