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未验证 提交 e041ffca 编写于 作者: J jjyaoao 提交者： GitHub
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remove paddle/infrt/ (#52719) 

* remove paddle/infrt/

* delete .lit_test_times.txt
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......@@ -73,7 +73,6 @@ tools/nvcc_lazy
# This file is automatically generated.
# TODO(zhiqiang) Move this file to build directory.
.lit_test_times.txt
paddle/fluid/pybind/eager_op_function.cc
tools/nvcc_lazy
......
paddle/infrt/tests/models/efficientnet-b4/net/utils.py 已删除 100644 → 0
浏览文件 @ 0cb0f70a
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import math
import re
from functools import partial
import paddle
import paddle.nn.functional as F
from paddle import nn
# Parameters for the entire model (stem, all blocks, and head)
GlobalParams = collections.namedtuple(
'GlobalParams',
[
'batch_norm_momentum',
'batch_norm_epsilon',
'dropout_rate',
'num_classes',
'width_coefficient',
'depth_coefficient',
'depth_divisor',
'min_depth',
'drop_connect_rate',
'image_size',
],
)
# Parameters for an individual model block
BlockArgs = collections.namedtuple(
'BlockArgs',
[
'kernel_size',
'num_repeat',
'input_filters',
'output_filters',
'expand_ratio',
'id_skip',
'stride',
'se_ratio',
],
)
# Change namedtuple defaults
GlobalParams.__new__.__defaults__ = (None,) * len(GlobalParams._fields)
BlockArgs.__new__.__defaults__ = (None,) * len(BlockArgs._fields)
def round_filters(filters, global_params):
"""Calculate and round number of filters based on depth multiplier."""
multiplier = global_params.width_coefficient
if not multiplier:
return filters
divisor = global_params.depth_divisor
min_depth = global_params.min_depth
filters *= multiplier
min_depth = min_depth or divisor
new_filters = max(
min_depth, int(filters + divisor / 2) // divisor * divisor
)
if new_filters < 0.9 * filters: # prevent rounding by more than 10%
new_filters += divisor
return int(new_filters)
def round_repeats(repeats, global_params):
"""Round number of filters based on depth multiplier."""
multiplier = global_params.depth_coefficient
if not multiplier:
return repeats
return int(math.ceil(multiplier * repeats))
def drop_connect(inputs, prob, training):
"""Drop input connection"""
if not training:
return inputs
keep_prob = 1.0 - prob
inputs_shape = paddle.shape(inputs)
random_tensor = keep_prob + paddle.rand(shape=[inputs_shape[0], 1, 1, 1])
binary_tensor = paddle.floor(random_tensor)
output = inputs / keep_prob * binary_tensor
return output
def get_same_padding_conv2d(image_size=None):
"""Chooses static padding if you have specified an image size, and dynamic padding otherwise.
Static padding is necessary for ONNX exporting of models."""
if image_size is None:
return Conv2dDynamicSamePadding
else:
return partial(Conv2dStaticSamePadding, image_size=image_size)
class Conv2dDynamicSamePadding(nn.Conv2D):
"""2D Convolutions like TensorFlow, for a dynamic image size"""
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
dilation=1,
groups=1,
bias_attr=None,
):
super().__init__(
in_channels,
out_channels,
kernel_size,
stride,
0,
dilation,
groups,
bias_attr=bias_attr,
)
self.stride = (
self._stride if len(self._stride) == 2 else [self._stride[0]] * 2
)
def forward(self, x):
ih, iw = x.shape[-2:]
kh, kw = self.weight.shape[-2:]
sh, sw = self.stride
oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
pad_h = max(
(oh - 1) * self.stride[0] + (kh - 1) * self._dilation[0] + 1 - ih, 0
)
pad_w = max(
(ow - 1) * self.stride[1] + (kw - 1) * self._dilation[1] + 1 - iw, 0
)
if pad_h > 0 or pad_w > 0:
x = F.pad(
x,
[
pad_w // 2,
pad_w - pad_w // 2,
pad_h // 2,
pad_h - pad_h // 2,
],
)
return F.conv2d(
x,
self.weight,
self.bias,
self.stride,
self._padding,
self._dilation,
self._groups,
)
class Conv2dStaticSamePadding(nn.Conv2D):
"""2D Convolutions like TensorFlow, for a fixed image size"""
def __init__(
self, in_channels, out_channels, kernel_size, image_size=None, **kwargs
):
if 'stride' in kwargs and isinstance(kwargs['stride'], list):
kwargs['stride'] = kwargs['stride'][0]
super().__init__(in_channels, out_channels, kernel_size, **kwargs)
self.stride = (
self._stride if len(self._stride) == 2 else [self._stride[0]] * 2
)
# Calculate padding based on image size and save it
assert image_size is not None
ih, iw = (
image_size if type(image_size) == list else [image_size, image_size]
)
kh, kw = self.weight.shape[-2:]
sh, sw = self.stride
oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
pad_h = max(
(oh - 1) * self.stride[0] + (kh - 1) * self._dilation[0] + 1 - ih, 0
)
pad_w = max(
(ow - 1) * self.stride[1] + (kw - 1) * self._dilation[1] + 1 - iw, 0
)
if pad_h > 0 or pad_w > 0:
self.static_padding = nn.Pad2D(
[pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2]
)
else:
self.static_padding = Identity()
def forward(self, x):
x = self.static_padding(x)
x = F.conv2d(
x,
self.weight,
self.bias,
self.stride,
self._padding,
self._dilation,
self._groups,
)
return x
class Identity(nn.Layer):
def __init__(
self,
):
super().__init__()
def forward(self, x):
return x
def efficientnet_params(model_name):
"""Map EfficientNet model name to parameter coefficients."""
params_dict = {
# Coefficients: width,depth,resolution,dropout
'efficientnet-b0': (1.0, 1.0, 224, 0.2),
'efficientnet-b1': (1.0, 1.1, 240, 0.2),
'efficientnet-b2': (1.1, 1.2, 260, 0.3),
'efficientnet-b3': (1.2, 1.4, 300, 0.3),
'efficientnet-b4': (1.4, 1.8, 380, 0.4),
'efficientnet-b5': (1.6, 2.2, 456, 0.4),
'efficientnet-b6': (1.8, 2.6, 528, 0.5),
'efficientnet-b7': (2.0, 3.1, 600, 0.5),
'efficientnet-b8': (2.2, 3.6, 672, 0.5),
'efficientnet-l2': (4.3, 5.3, 800, 0.5),
}
return params_dict[model_name]
class BlockDecoder:
"""Block Decoder for readability, straight from the official TensorFlow repository"""
@staticmethod
def _decode_block_string(block_string):
"""Gets a block through a string notation of arguments."""
assert isinstance(block_string, str)
ops = block_string.split('_')
options = {}
for op in ops:
splits = re.split(r'(\d.*)', op)
if len(splits) >= 2:
key, value = splits[:2]
options[key] = value
# Check stride
assert ('s' in options and len(options['s']) == 1) or (
len(options['s']) == 2 and options['s'][0] == options['s'][1]
)
return BlockArgs(
kernel_size=int(options['k']),
num_repeat=int(options['r']),
input_filters=int(options['i']),
output_filters=int(options['o']),
expand_ratio=int(options['e']),
id_skip=('noskip' not in block_string),
se_ratio=float(options['se']) if 'se' in options else None,
stride=[int(options['s'][0])],
)
@staticmethod
def _encode_block_string(block):
"""Encodes a block to a string."""
args = [
'r%d' % block.num_repeat,
'k%d' % block.kernel_size,
's%d%d' % (block.strides[0], block.strides[1]),
'e%s' % block.expand_ratio,
'i%d' % block.input_filters,
'o%d' % block.output_filters,
]
if 0 < block.se_ratio <= 1:
args.append('se%s' % block.se_ratio)
if block.id_skip is False:
args.append('noskip')
return '_'.join(args)
@staticmethod
def decode(string_list):
"""
Decodes a list of string notations to specify blocks inside the network.
:param string_list: a list of strings, each string is a notation of block
:return: a list of BlockArgs namedtuples of block args
"""
assert isinstance(string_list, list)
blocks_args = []
for block_string in string_list:
blocks_args.append(BlockDecoder._decode_block_string(block_string))
return blocks_args
@staticmethod
def encode(blocks_args):
"""
Encodes a list of BlockArgs to a list of strings.
:param blocks_args: a list of BlockArgs namedtuples of block args
:return: a list of strings, each string is a notation of block
"""
block_strings = []
for block in blocks_args:
block_strings.append(BlockDecoder._encode_block_string(block))
return block_strings
def efficientnet(
width_coefficient=None,
depth_coefficient=None,
dropout_rate=0.2,
drop_connect_rate=0.2,
image_size=None,
num_classes=1000,
):
"""Get block arguments according to parameter and coefficients."""
blocks_args = [
'r1_k3_s11_e1_i32_o16_se0.25',
'r2_k3_s22_e6_i16_o24_se0.25',
'r2_k5_s22_e6_i24_o40_se0.25',
'r3_k3_s22_e6_i40_o80_se0.25',
'r3_k5_s11_e6_i80_o112_se0.25',
'r4_k5_s22_e6_i112_o192_se0.25',
'r1_k3_s11_e6_i192_o320_se0.25',
]
blocks_args = BlockDecoder.decode(blocks_args)
global_params = GlobalParams(
batch_norm_momentum=0.99,
batch_norm_epsilon=1e-3,
dropout_rate=dropout_rate,
drop_connect_rate=drop_connect_rate,
num_classes=num_classes,
width_coefficient=width_coefficient,
depth_coefficient=depth_coefficient,
depth_divisor=8,
min_depth=None,
image_size=image_size,
)
return blocks_args, global_params
def get_model_params(model_name, override_params):
"""Get the block args and global params for a given model"""
if model_name.startswith('efficientnet'):
w, d, s, p = efficientnet_params(model_name)
blocks_args, global_params = efficientnet(
width_coefficient=w,
depth_coefficient=d,
dropout_rate=p,
image_size=s,
)
else:
raise NotImplementedError(
'model name is not pre-defined: %s' % model_name
)
if override_params:
global_params = global_params._replace(**override_params)
return blocks_args, global_params
url_map = {
'efficientnet-b0': '/home/aistudio/data/weights/efficientnet-b0-355c32eb.pdparams',
'efficientnet-b1': '/home/aistudio/data/weights/efficientnet-b1-f1951068.pdparams',
'efficientnet-b2': '/home/aistudio/data/weights/efficientnet-b2-8bb594d6.pdparams',
'efficientnet-b3': '/home/aistudio/data/weights/efficientnet-b3-5fb5a3c3.pdparams',
'efficientnet-b4': '/home/aistudio/data/weights/efficientnet-b4-6ed6700e.pdparams',
'efficientnet-b5': '/home/aistudio/data/weights/efficientnet-b5-b6417697.pdparams',
'efficientnet-b6': '/home/aistudio/data/weights/efficientnet-b6-c76e70fd.pdparams',
'efficientnet-b7': '/home/aistudio/data/weights/efficientnet-b7-dcc49843.pdparams',
}
url_map_advprop = {
'efficientnet-b0': '/home/aistudio/data/weights/adv-efficientnet-b0-b64d5a18.pdparams',
'efficientnet-b1': '/home/aistudio/data/weights/adv-efficientnet-b1-0f3ce85a.pdparams',
'efficientnet-b2': '/home/aistudio/data/weights/adv-efficientnet-b2-6e9d97e5.pdparams',
'efficientnet-b3': '/home/aistudio/data/weights/adv-efficientnet-b3-cdd7c0f4.pdparams',
'efficientnet-b4': '/home/aistudio/data/weights/adv-efficientnet-b4-44fb3a87.pdparams',
'efficientnet-b5': '/home/aistudio/data/weights/adv-efficientnet-b5-86493f6b.pdparams',
'efficientnet-b6': '/home/aistudio/data/weights/adv-efficientnet-b6-ac80338e.pdparams',
'efficientnet-b7': '/home/aistudio/data/weights/adv-efficientnet-b7-4652b6dd.pdparams',
'efficientnet-b8': '/home/aistudio/data/weights/adv-efficientnet-b8-22a8fe65.pdparams',
}
def load_pretrained_weights(
model, model_name, weights_path=None, load_fc=True, advprop=False
):
"""Loads pretrained weights from weights path or download using url.
Args:
model (Module): The whole model of efficientnet.
model_name (str): Model name of efficientnet.
weights_path (None or str):
str: path to pretrained weights file on the local disk.
None: use pretrained weights downloaded from the Internet.
load_fc (bool): Whether to load pretrained weights for fc layer at the end of the model.
advprop (bool): Whether to load pretrained weights
trained with advprop (valid when weights_path is None).
"""
# AutoAugment or Advprop (different preprocessing)
url_map_ = url_map_advprop if advprop else url_map
state_dict = paddle.load(url_map_[model_name])
if load_fc:
model.set_state_dict(state_dict)
else:
state_dict.pop('_fc.weight')
state_dict.pop('_fc.bias')
model.set_state_dict(state_dict)
print(f'Loaded pretrained weights for {model_name}')
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