mirrors
/
PaddleClas
mirror of https://github.com/PaddlePaddle/PaddleClas.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

Merge branch 'develop' into dev/doc2

pull/1580/head
Wu Sibo 2022-01-04 10:14:52 +08:00 committed by GitHub
parent 4d1dad6abb 56cb7b3d2d
commit ce3b904760
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
35 changed files with 413 additions and 176 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

20
deploy/configs/inference_product_binary.yaml → deploy/configs/inference_general_binary.yaml
View File

@ -1,7 +1,7 @@
Global:
infer_imgs: "./recognition_demo_data_v1.1/test_product/daoxiangcunjinzhubing_6.jpg"
det_inference_model_dir: "./models/ppyolov2_r50vd_dcn_mainbody_v1.0_infer"
rec_inference_model_dir: "./models/product_MV3_x1_0_aliproduct_bin_v1.0_infer"
infer_imgs: "./drink_dataset_v1.0/test_images/001.jpeg"
det_inference_model_dir: "./models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer"
rec_inference_model_dir: "./models/general_PPLCNet_x2_5_lite_binary_v1.0_infer"
rec_nms_thresold: 0.05
batch_size: 1
@ -11,7 +11,6 @@ Global:
labe_list:
- foreground
# inference engine config
use_gpu: True
enable_mkldnn: True
cpu_num_threads: 10
@ -49,19 +48,18 @@ RecPreProcess:
RecPostProcess:
main_indicator: Binarize
Binarize:
method: "round"
method: "sign"
# indexing engine config
IndexProcess:
index_method: "Flat" # supported: HNSW32, Flat
index_dir: "./recognition_demo_data_v1.1/gallery_product/index_binary"
image_root: "./recognition_demo_data_v1.1/gallery_product/"
data_file: "./recognition_demo_data_v1.1/gallery_product/data_file.txt"
index_method: "Flat" # supported: HNSW32, Flat
image_root: "./drink_dataset_v1.0/gallery/"
index_dir: "./drink_dataset_v1.0/index_bin"
data_file: "./drink_dataset_v1.0/gallery/drink_label.txt"
index_operation: "new" # suported: "append", "remove", "new"
delimiter: "\t"
dist_type: "hamming"
embedding_size: 512
batch_size: 32
binary_index: true
return_k: 5
score_thres: 0
hamming_radius: 100

23
deploy/python/predict_system.py
View File

@ -47,14 +47,14 @@ class SystemPredictor(object):
index_dir, "vector.index")), "vector.index not found ..."
assert os.path.exists(os.path.join(
index_dir, "id_map.pkl")), "id_map.pkl not found ... "
if config['IndexProcess'].get("binary_index", False):
if config['IndexProcess'].get("dist_type") == "hamming":
self.Searcher = faiss.read_index_binary(
os.path.join(index_dir, "vector.index"))
else:
self.Searcher = faiss.read_index(
os.path.join(index_dir, "vector.index"))
with open(os.path.join(index_dir, "id_map.pkl"), "rb") as fd:
self.id_map = pickle.load(fd)
@ -111,12 +111,19 @@ class SystemPredictor(object):
rec_results = self.rec_predictor.predict(crop_img)
preds["bbox"] = [xmin, ymin, xmax, ymax]
scores, docs = self.Searcher.search(rec_results, self.return_k)
# just top-1 result will be returned for the final
if scores[0][0] >= self.config["IndexProcess"]["score_thres"]:
preds["rec_docs"] = self.id_map[docs[0][0]].split()[1]
preds["rec_scores"] = scores[0][0]
output.append(preds)
if self.config["IndexProcess"]["dist_type"] == "hamming":
if scores[0][0] <= self.config["IndexProcess"][
"hamming_radius"]:
preds["rec_docs"] = self.id_map[docs[0][0]].split()[1]
preds["rec_scores"] = scores[0][0]
output.append(preds)
else:
if scores[0][0] >= self.config["IndexProcess"]["score_thres"]:
preds["rec_docs"] = self.id_map[docs[0][0]].split()[1]
preds["rec_scores"] = scores[0][0]
output.append(preds)
# st5: nms to the final results to avoid fetching duplicate results
output = self.nms_to_rec_results(

BIN
docs/images/wx_group.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 57 KiB

After

Width:  |  Height:  |  Size: 202 KiB

4
docs/zh_CN/quick_start/quick_start_recognition.md
View File

@ -40,8 +40,9 @@
| 模型简介 | 推荐场景 | inference 模型 | 预测配置文件 |
| ------------ | ------------- | -------- | ------- |
| 轻量级通用主体检测模型 | 通用场景 |[tar 格式文件下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar) [zip 格式文件下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.zip) | - |
| 轻量级通用主体检测模型 | 通用场景 |[tar 格式下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar) [zip 格式文件下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.zip) | - |
| 轻量级通用识别模型 | 通用场景 | [tar 格式下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar) [zip 格式文件下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.zip) | [inference_general.yaml](../../../deploy/configs/inference_general.yaml) |
| 轻量级通用识别二值模型 | 检索库很大， 存储受限场景 | [tar 格式下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_binary_v1.0_infer.tar) [zip 格式文件下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_binary_v1.0_infer.zip)| [inference_general_binary.yaml](../../../deploy/configs/inference_general_binary.yaml) |
注意：由于部分解压缩软件在解压上述 `tar` 格式文件时存在问题，建议非命令行用户下载 `zip` 格式文件并解压。`tar` 格式文件建议使用命令 `tar xf xxx.tar` 解压。
@ -331,4 +332,3 @@ wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognit
按照上述步骤下载模型和测试数据后，您可以进行相关方向识别模型的测试。
* 更多关于主体检测的介绍可以参考：[主体检测教程文档](../image_recognition_pipeline/mainbody_detection.md)；关于特征提取的介绍可以参考：[特征提取教程文档](../image_recognition_pipeline/feature_extraction.md)；关于向量检索的介绍可以参考：[向量检索教程文档](../image_recognition_pipeline/vector_search.md)。

335
ppcls/arch/backbone/base/theseus_layer.py
View File

@ -1,6 +1,20 @@
from abc import ABC
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from typing import Tuple, List, Dict, Union, Callable, Any
from paddle import nn
import re
from ppcls.utils import logger
class Identity(nn.Layer):
@ -19,110 +33,239 @@ class TheseusLayer(nn.Layer):
self.pruner = None
self.quanter = None
# stop doesn't work when stop layer has a parallel branch.
def stop_after(self, stop_layer_name: str):
after_stop = False
for layer_i in self._sub_layers:
if after_stop:
self._sub_layers[layer_i] = Identity()
continue
layer_name = self._sub_layers[layer_i].full_name()
if layer_name == stop_layer_name:
after_stop = True
continue
if isinstance(self._sub_layers[layer_i], TheseusLayer):
after_stop = self._sub_layers[layer_i].stop_after(
stop_layer_name)
return after_stop
def update_res(self, return_patterns):
for return_pattern in return_patterns:
pattern_list = return_pattern.split(".")
if not pattern_list:
continue
sub_layer_parent = self
while len(pattern_list) > 1:
if '[' in pattern_list[0]:
sub_layer_name = pattern_list[0].split('[')[0]
sub_layer_index = pattern_list[0].split('[')[1].split(']')[0]
sub_layer_parent = getattr(sub_layer_parent, sub_layer_name)[sub_layer_index]
else:
sub_layer_parent = getattr(sub_layer_parent, pattern_list[0],
None)
if sub_layer_parent is None:
break
if isinstance(sub_layer_parent, WrapLayer):
sub_layer_parent = sub_layer_parent.sub_layer
pattern_list = pattern_list[1:]
if sub_layer_parent is None:
continue
if '[' in pattern_list[0]:
sub_layer_name = pattern_list[0].split('[')[0]
sub_layer_index = pattern_list[0].split('[')[1].split(']')[0]
sub_layer = getattr(sub_layer_parent, sub_layer_name)[sub_layer_index]
if not isinstance(sub_layer, TheseusLayer):
sub_layer = wrap_theseus(sub_layer)
getattr(sub_layer_parent, sub_layer_name)[sub_layer_index] = sub_layer
else:
sub_layer = getattr(sub_layer_parent, pattern_list[0])
if not isinstance(sub_layer, TheseusLayer):
sub_layer = wrap_theseus(sub_layer)
setattr(sub_layer_parent, pattern_list[0], sub_layer)
sub_layer.res_dict = self.res_dict
sub_layer.res_name = return_pattern
sub_layer.register_forward_post_hook(sub_layer._save_sub_res_hook)
def _return_dict_hook(self, layer, input, output):
res_dict = {"output": output}
# 'list' is needed to avoid error raised by popping self.res_dict
for res_key in list(self.res_dict):
# clear the res_dict because the forward process may change according to input
res_dict[res_key] = self.res_dict.pop(res_key)
return res_dict
def _save_sub_res_hook(self, layer, input, output):
self.res_dict[self.res_name] = output
def _return_dict_hook(self, layer, input, output):
res_dict = {"output": output}
for res_key in list(self.res_dict):
res_dict[res_key] = self.res_dict.pop(res_key)
def replace_sub(self, *args, **kwargs) -> None:
msg = "The function 'replace_sub()' is deprecated, please use 'upgrade_sublayer()' instead."
logger.error(DeprecationWarning(msg))
raise DeprecationWarning(msg)
def upgrade_sublayer(self,
layer_name_pattern: Union[str, List[str]],
handle_func: Callable[[nn.Layer, str], nn.Layer]
) -> Dict[str, nn.Layer]:
"""use 'handle_func' to modify the sub-layer(s) specified by 'layer_name_pattern'.
Args:
layer_name_pattern (Union[str, List[str]]): The name of layer to be modified by 'handle_func'.
handle_func (Callable[[nn.Layer, str], nn.Layer]): The function to modify target layer specified by 'layer_name_pattern'. The formal params are the layer(nn.Layer) and pattern(str) that is (a member of) layer_name_pattern (when layer_name_pattern is List type). And the return is the layer processed.
Returns:
Dict[str, nn.Layer]: The key is the pattern and corresponding value is the result returned by 'handle_func()'.
Examples:
from paddle import nn
import paddleclas
def rep_func(layer: nn.Layer, pattern: str):
new_layer = nn.Conv2D(
in_channels=layer._in_channels,
out_channels=layer._out_channels,
kernel_size=5,
padding=2
)
return new_layer
net = paddleclas.MobileNetV1()
res = net.replace_sub(layer_name_pattern=["blocks[11].depthwise_conv.conv", "blocks[12].depthwise_conv.conv"], handle_func=rep_func)
print(res)
# {'blocks[11].depthwise_conv.conv': the corresponding new_layer, 'blocks[12].depthwise_conv.conv': the corresponding new_layer}
"""
if not isinstance(layer_name_pattern, list):
layer_name_pattern = [layer_name_pattern]
handle_res_dict = {}
for pattern in layer_name_pattern:
# parse pattern to find target layer and its parent
layer_list = parse_pattern_str(pattern=pattern, parent_layer=self)
if not layer_list:
continue
sub_layer_parent = layer_list[-2]["layer"] if len(
layer_list) > 1 else self
sub_layer = layer_list[-1]["layer"]
sub_layer_name = layer_list[-1]["name"]
sub_layer_index = layer_list[-1]["index"]
new_sub_layer = handle_func(sub_layer, pattern)
if sub_layer_index:
getattr(sub_layer_parent,
sub_layer_name)[sub_layer_index] = new_sub_layer
else:
setattr(sub_layer_parent, sub_layer_name, new_sub_layer)
handle_res_dict[pattern] = new_sub_layer
return handle_res_dict
def stop_after(self, stop_layer_name: str) -> bool:
"""stop forward and backward after 'stop_layer_name'.
Args:
stop_layer_name (str): The name of layer that stop forward and backward after this layer.
Returns:
bool: 'True' if successful, 'False' otherwise.
"""
layer_list = parse_pattern_str(stop_layer_name, self)
if not layer_list:
return False
parent_layer = self
for layer_dict in layer_list:
name, index = layer_dict["name"], layer_dict["index"]
if not set_identity(parent_layer, name, index):
msg = f"Failed to set the layers that after stop_layer_name('{stop_layer_name}') to IdentityLayer. The error layer's name is '{name}'."
logger.warning(msg)
return False
parent_layer = layer_dict["layer"]
return True
def update_res(
self,
return_patterns: Union[str, List[str]]) -> Dict[str, nn.Layer]:
"""update the result(s) to be returned.
Args:
return_patterns (Union[str, List[str]]): The name of layer to return output.
Returns:
Dict[str, nn.Layer]: The pattern(str) and corresponding layer(nn.Layer) that have been set successfully.
"""
# clear res_dict that could have been set
self.res_dict = {}
class Handler(object):
def __init__(self, res_dict):
# res_dict is a reference
self.res_dict = res_dict
def __call__(self, layer, pattern):
layer.res_dict = self.res_dict
layer.res_name = pattern
if hasattr(layer, "hook_remove_helper"):
layer.hook_remove_helper.remove()
layer.hook_remove_helper = layer.register_forward_post_hook(
layer._save_sub_res_hook)
return layer
handle_func = Handler(self.res_dict)
res_dict = self.upgrade_sublayer(
return_patterns, handle_func=handle_func)
if hasattr(self, "hook_remove_helper"):
self.hook_remove_helper.remove()
self.hook_remove_helper = self.register_forward_post_hook(
self._return_dict_hook)
return res_dict
def replace_sub(self, layer_name_pattern, replace_function,
recursive=True):
for layer_i in self._sub_layers:
layer_name = self._sub_layers[layer_i].full_name()
if re.match(layer_name_pattern, layer_name):
self._sub_layers[layer_i] = replace_function(self._sub_layers[
layer_i])
if recursive:
if isinstance(self._sub_layers[layer_i], TheseusLayer):
self._sub_layers[layer_i].replace_sub(
layer_name_pattern, replace_function, recursive)
elif isinstance(self._sub_layers[layer_i],
(nn.Sequential, nn.LayerList)):
for layer_j in self._sub_layers[layer_i]._sub_layers:
self._sub_layers[layer_i]._sub_layers[
layer_j].replace_sub(layer_name_pattern,
replace_function, recursive)
'''
example of replace function:
def replace_conv(origin_conv: nn.Conv2D):
new_conv = nn.Conv2D(
in_channels=origin_conv._in_channels,
out_channels=origin_conv._out_channels,
kernel_size=origin_conv._kernel_size,
stride=2
)
return new_conv
def set_identity(parent_layer: nn.Layer,
layer_name: str,
layer_index: str=None) -> bool:
"""set the layer specified by layer_name and layer_index to Indentity.
'''
Args:
parent_layer (nn.Layer): The parent layer of target layer specified by layer_name and layer_index.
layer_name (str): The name of target layer to be set to Indentity.
layer_index (str, optional): The index of target layer to be set to Indentity in parent_layer. Defaults to None.
Returns:
bool: True if successfully, False otherwise.
"""
stop_after = False
for sub_layer_name in parent_layer._sub_layers:
if stop_after:
parent_layer._sub_layers[sub_layer_name] = Identity()
continue
if sub_layer_name == layer_name:
stop_after = True
if layer_index and stop_after:
stop_after = False
for sub_layer_index in parent_layer._sub_layers[
layer_name]._sub_layers:
if stop_after:
parent_layer._sub_layers[layer_name][
sub_layer_index] = Identity()
continue
if layer_index == sub_layer_index:
stop_after = True
return stop_after
class WrapLayer(TheseusLayer):
def __init__(self, sub_layer):
super(WrapLayer, self).__init__()
self.sub_layer = sub_layer
def parse_pattern_str(pattern: str, parent_layer: nn.Layer) -> Union[
None, List[Dict[str, Union[nn.Layer, str, None]]]]:
"""parse the string type pattern.
def forward(self, *inputs, **kwargs):
return self.sub_layer(*inputs, **kwargs)
Args:
pattern (str): The pattern to discribe layer.
parent_layer (nn.Layer): The root layer relative to the pattern.
Returns:
Union[None, List[Dict[str, Union[nn.Layer, str, None]]]]: None if failed. If successfully, the members are layers parsed in order:
[
{"layer": first layer, "name": first layer's name parsed, "index": first layer's index parsed if exist},
{"layer": second layer, "name": second layer's name parsed, "index": second layer's index parsed if exist},
...
]
"""
def wrap_theseus(sub_layer):
wrapped_layer = WrapLayer(sub_layer)
return wrapped_layer
pattern_list = pattern.split(".")
if not pattern_list:
msg = f"The pattern('{pattern}') is illegal. Please check and retry."
logger.warning(msg)
return None
layer_list = []
while len(pattern_list) > 0:
if '[' in pattern_list[0]:
target_layer_name = pattern_list[0].split('[')[0]
target_layer_index = pattern_list[0].split('[')[1].split(']')[0]
else:
target_layer_name = pattern_list[0]
target_layer_index = None
target_layer = getattr(parent_layer, target_layer_name, None)
if target_layer is None:
msg = f"Not found layer named('{target_layer_name}') specifed in pattern('{pattern}')."
logger.warning(msg)
return None
if target_layer_index and target_layer:
if int(target_layer_index) < 0 or int(target_layer_index) >= len(
target_layer):
msg = f"Not found layer by index('{target_layer_index}') specifed in pattern('{pattern}'). The index should < {len(target_layer)} and > 0."
logger.warning(msg)
return None
target_layer = target_layer[target_layer_index]
layer_list.append({
"layer": target_layer,
"name": target_layer_name,
"index": target_layer_index
})
pattern_list = pattern_list[1:]
parent_layer = target_layer
return layer_list

6
ppcls/arch/backbone/legendary_models/esnet.py
View File

@ -217,7 +217,8 @@ class ESNet(TheseusLayer):
class_num=1000,
scale=1.0,
dropout_prob=0.2,
class_expand=1280):
class_expand=1280,
return_patterns=None):
super().__init__()
self.scale = scale
self.class_num = class_num
@ -268,6 +269,9 @@ class ESNet(TheseusLayer):
self.flatten = nn.Flatten(start_axis=1, stop_axis=-1)
self.fc = Linear(self.class_expand, self.class_num)
if return_patterns is not None:
self.update_res(return_patterns)
def forward(self, x):
x = self.conv1(x)
x = self.max_pool(x)

11
ppcls/arch/backbone/legendary_models/hrnet.py
View File

@ -244,7 +244,7 @@ class HighResolutionModule(TheseusLayer):
for i in range(len(num_filters)):
self.basic_block_list.append(
nn.Sequential(*[
nn.Sequential(* [
BasicBlock(
num_channels=num_filters[i],
num_filters=num_filters[i],
@ -367,7 +367,11 @@ class HRNet(TheseusLayer):
model: nn.Layer. Specific HRNet model depends on args.
"""
def __init__(self, width=18, has_se=False, class_num=1000, return_patterns=None):
def __init__(self,
width=18,
has_se=False,
class_num=1000,
return_patterns=None):
super().__init__()
self.width = width
@ -394,7 +398,7 @@ class HRNet(TheseusLayer):
stride=2,
act="relu")
self.layer1 = nn.Sequential(*[
self.layer1 = nn.Sequential(* [
BottleneckBlock(
num_channels=64 if i == 0 else 256,
num_filters=64,
@ -458,7 +462,6 @@ class HRNet(TheseusLayer):
weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)))
if return_patterns is not None:
self.update_res(return_patterns)
self.register_forward_post_hook(self._return_dict_hook)
def forward(self, x):
x = self.conv_layer1_1(x)

1
ppcls/arch/backbone/legendary_models/inception_v3.py
View File

@ -498,7 +498,6 @@ class Inception_V3(TheseusLayer):
bias_attr=ParamAttr())
if return_patterns is not None:
self.update_res(return_patterns)
self.register_forward_post_hook(self._return_dict_hook)
def forward(self, x):
x = self.inception_stem(x)

3
ppcls/arch/backbone/legendary_models/mobilenet_v1.py
View File

@ -128,7 +128,7 @@ class MobileNet(TheseusLayer):
[int(512 * scale), 512, 1024, 512, 2],
[int(1024 * scale), 1024, 1024, 1024, 1]]
self.blocks = nn.Sequential(*[
self.blocks = nn.Sequential(* [
DepthwiseSeparable(
num_channels=params[0],
num_filters1=params[1],
@ -147,7 +147,6 @@ class MobileNet(TheseusLayer):
weight_attr=ParamAttr(initializer=KaimingNormal()))
if return_patterns is not None:
self.update_res(return_patterns)
self.register_forward_post_hook(self._return_dict_hook)
def forward(self, x):
x = self.conv(x)

1
ppcls/arch/backbone/legendary_models/mobilenet_v3.py
View File

@ -202,7 +202,6 @@ class MobileNetV3(TheseusLayer):
self.fc = Linear(self.class_expand, class_num)
if return_patterns is not None:
self.update_res(return_patterns)
self.register_forward_post_hook(self._return_dict_hook)
def forward(self, x):
x = self.conv(x)

16
ppcls/arch/backbone/legendary_models/pp_lcnet.py
View File

@ -171,7 +171,8 @@ class PPLCNet(TheseusLayer):
scale=1.0,
class_num=1000,
dropout_prob=0.2,
class_expand=1280):
class_expand=1280,
return_patterns=None):
super().__init__()
self.scale = scale
self.class_expand = class_expand
@ -182,7 +183,7 @@ class PPLCNet(TheseusLayer):
num_filters=make_divisible(16 * scale),
stride=2)
self.blocks2 = nn.Sequential(*[
self.blocks2 = nn.Sequential(* [
DepthwiseSeparable(
num_channels=make_divisible(in_c * scale),
num_filters=make_divisible(out_c * scale),
@ -192,7 +193,7 @@ class PPLCNet(TheseusLayer):
for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks2"])
])
self.blocks3 = nn.Sequential(*[
self.blocks3 = nn.Sequential(* [
DepthwiseSeparable(
num_channels=make_divisible(in_c * scale),
num_filters=make_divisible(out_c * scale),
@ -202,7 +203,7 @@ class PPLCNet(TheseusLayer):
for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks3"])
])
self.blocks4 = nn.Sequential(*[
self.blocks4 = nn.Sequential(* [
DepthwiseSeparable(
num_channels=make_divisible(in_c * scale),
num_filters=make_divisible(out_c * scale),
@ -212,7 +213,7 @@ class PPLCNet(TheseusLayer):
for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks4"])
])
self.blocks5 = nn.Sequential(*[
self.blocks5 = nn.Sequential(* [
DepthwiseSeparable(
num_channels=make_divisible(in_c * scale),
num_filters=make_divisible(out_c * scale),
@ -222,7 +223,7 @@ class PPLCNet(TheseusLayer):
for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks5"])
])
self.blocks6 = nn.Sequential(*[
self.blocks6 = nn.Sequential(* [
DepthwiseSeparable(
num_channels=make_divisible(in_c * scale),
num_filters=make_divisible(out_c * scale),
@ -248,6 +249,9 @@ class PPLCNet(TheseusLayer):
self.fc = Linear(self.class_expand, class_num)
if return_patterns is not None:
self.update_res(return_patterns)
def forward(self, x):
x = self.conv1(x)

1
ppcls/arch/backbone/legendary_models/resnet.py
View File

@ -340,7 +340,6 @@ class ResNet(TheseusLayer):
self.data_format = data_format
if return_patterns is not None:
self.update_res(return_patterns)
self.register_forward_post_hook(self._return_dict_hook)
def forward(self, x):
with paddle.static.amp.fp16_guard():

7
ppcls/arch/backbone/legendary_models/vgg.py
View File

@ -111,7 +111,11 @@ class VGGNet(TheseusLayer):
model: nn.Layer. Specific VGG model depends on args.
"""
def __init__(self, config, stop_grad_layers=0, class_num=1000, return_patterns=None):
def __init__(self,
config,
stop_grad_layers=0,
class_num=1000,
return_patterns=None):
super().__init__()
self.stop_grad_layers = stop_grad_layers
@ -139,7 +143,6 @@ class VGGNet(TheseusLayer):
self.fc3 = Linear(4096, class_num)
if return_patterns is not None:
self.update_res(return_patterns)
self.register_forward_post_hook(self._return_dict_hook)
def forward(self, inputs):
x = self.conv_block_1(inputs)

6
ppcls/arch/backbone/variant_models/pp_lcnet_variant.py
View File

@ -19,11 +19,11 @@ class TanhSuffix(paddle.nn.Layer):
def PPLCNet_x2_5_Tanh(pretrained=False, use_ssld=False, **kwargs):
def replace_function(origin_layer):
def replace_function(origin_layer, pattern):
new_layer = TanhSuffix(origin_layer)
return new_layer
match_re = "linear_0"
pattern = "fc"
model = PPLCNet_x2_5(pretrained=pretrained, use_ssld=use_ssld, **kwargs)
model.replace_sub(match_re, replace_function, True)
model.upgrade_sublayer(pattern, replace_function)
return model

6
ppcls/arch/backbone/variant_models/resnet_variant.py
View File

@ -5,7 +5,7 @@ __all__ = ["ResNet50_last_stage_stride1"]
def ResNet50_last_stage_stride1(pretrained=False, use_ssld=False, **kwargs):
def replace_function(conv):
def replace_function(conv, pattern):
new_conv = Conv2D(
in_channels=conv._in_channels,
out_channels=conv._out_channels,
@ -16,8 +16,8 @@ def ResNet50_last_stage_stride1(pretrained=False, use_ssld=False, **kwargs):
bias_attr=conv._bias_attr)
return new_conv
match_re = "conv2d_4[4|6]"
pattern = ["blocks[13].conv1.conv", "blocks[13].short.conv"]
model = ResNet50(pretrained=False, use_ssld=use_ssld, **kwargs)
model.replace_sub(match_re, replace_function, True)
model.upgrade_sublayer(pattern, replace_function)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet50"], use_ssld)
return model

22
ppcls/arch/backbone/variant_models/vgg_variant.py
View File

@ -1,28 +1,28 @@
import paddle
from paddle.nn import Sigmoid
from ppcls.arch.backbone.legendary_models.vgg import VGG19
__all__ = ["VGG19Sigmoid"]
class SigmoidSuffix(paddle.nn.Layer):
def __init__(self, origin_layer):
super(SigmoidSuffix, self).__init__()
super().__init__()
self.origin_layer = origin_layer
self.sigmoid = Sigmoid()
def forward(self, input, res_dict=None, **kwargs):
x = self.origin_layer(input)
x = self.sigmoid(x)
return x
def VGG19Sigmoid(pretrained=False, use_ssld=False, **kwargs):
def replace_function(origin_layer):
def replace_function(origin_layer, pattern):
new_layer = SigmoidSuffix(origin_layer)
return new_layer
match_re = "linear_2"
pattern = "fc2"
model = VGG19(pretrained=pretrained, use_ssld=use_ssld, **kwargs)
model.replace_sub(match_re, replace_function, True)
model.upgrade_sublayer(pattern, replace_function)
return model

2
ppcls/configs/Cartoonface/ResNet50_icartoon.yaml
View File

@ -22,7 +22,7 @@ Arch:
name: "ResNet50"
pretrained: True
BackboneStopLayer:
name: "flatten_0"
name: "flatten"
output_dim: 2048
Head:
name: "FC"

2
ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml
View File

@ -28,7 +28,7 @@ Arch:
pretrained: True
use_ssld: True
BackboneStopLayer:
name: flatten_0
name: "flatten"
Neck:
name: FC
embedding_size: 1280

2
ppcls/configs/Logo/ResNet50_ReID.yaml
View File

@ -24,7 +24,7 @@ Arch:
name: "ResNet50_last_stage_stride1"
pretrained: True
BackboneStopLayer:
name: "adaptive_avg_pool2d_0"
name: "avg_pool"
Neck:
name: "VehicleNeck"
in_channels: 2048

2
ppcls/configs/Products/ResNet50_vd_Aliproduct.yaml
View File

@ -25,7 +25,7 @@ Arch:
name: ResNet50_vd
pretrained: True
BackboneStopLayer:
name: flatten_0
name: "flatten"
Neck:
name: FC
embedding_size: 2048

2
ppcls/configs/Products/ResNet50_vd_Inshop.yaml
View File

@ -25,7 +25,7 @@ Arch:
name: ResNet50_vd
pretrained: False
BackboneStopLayer:
name: flatten_0
name: "flatten"
Neck:
name: FC
embedding_size: 2048

2
ppcls/configs/Products/ResNet50_vd_SOP.yaml
View File

@ -22,7 +22,7 @@ Arch:
name: ResNet50_vd
pretrained: False
BackboneStopLayer:
name: flatten_0
name: "flatten"
Neck:
name: FC
embedding_size: 2048

2
ppcls/configs/Vehicle/PPLCNet_2.5x_ReID.yaml
View File

@ -27,7 +27,7 @@ Arch:
pretrained: True
use_ssld: True
BackboneStopLayer:
name: "flatten_0"
name: "flatten"
Neck:
name: "FC"
embedding_size: 1280

2
ppcls/configs/Vehicle/ResNet50.yaml
View File

@ -23,7 +23,7 @@ Arch:
name: "ResNet50_last_stage_stride1"
pretrained: True
BackboneStopLayer:
name: "adaptive_avg_pool2d_0"
name: "avg_pool"
Neck:
name: "VehicleNeck"
in_channels: 2048

2
ppcls/configs/Vehicle/ResNet50_ReID.yaml
View File

@ -24,7 +24,7 @@ Arch:
name: "ResNet50_last_stage_stride1"
pretrained: True
BackboneStopLayer:
name: "adaptive_avg_pool2d_0"
name: "avg_pool"
Neck:
name: "VehicleNeck"
in_channels: 2048

2
ppcls/configs/quick_start/MobileNetV1_retrieval.yaml
View File

@ -25,7 +25,7 @@ Arch:
name: MobileNetV1
pretrained: False
BackboneStopLayer:
name: flatten_0
name: "flatten"
Neck:
name: FC
embedding_size: 1024

2
ppcls/configs/slim/GeneralRecognition_PPLCNet_x2_5_quantization.yaml
View File

@ -34,7 +34,7 @@ Arch:
pretrained: False
use_ssld: True
BackboneStopLayer:
name: flatten_0
name: "flatten"
Neck:
name: FC
embedding_size: 1280

2
ppcls/configs/slim/ResNet50_vehicle_cls_prune.yaml
View File

@ -28,7 +28,7 @@ Arch:
name: "ResNet50_last_stage_stride1"
pretrained: True
BackboneStopLayer:
name: "adaptive_avg_pool2d_0"
name: "avg_pool"
Neck:
name: "VehicleNeck"
in_channels: 2048

2
ppcls/configs/slim/ResNet50_vehicle_cls_quantization.yaml
View File

@ -27,7 +27,7 @@ Arch:
name: "ResNet50_last_stage_stride1"
pretrained: True
BackboneStopLayer:
name: "adaptive_avg_pool2d_0"
name: "avg_pool"
Neck:
name: "VehicleNeck"
in_channels: 2048

2
ppcls/configs/slim/ResNet50_vehicle_reid_prune.yaml
View File

@ -31,7 +31,7 @@ Arch:
name: "ResNet50_last_stage_stride1"
pretrained: True
BackboneStopLayer:
name: "adaptive_avg_pool2d_0"
name: "avg_pool"
Neck:
name: "VehicleNeck"
in_channels: 2048

2
ppcls/configs/slim/ResNet50_vehicle_reid_quantization.yaml
View File

@ -30,7 +30,7 @@ Arch:
name: "ResNet50_last_stage_stride1"
pretrained: True
BackboneStopLayer:
name: "adaptive_avg_pool2d_0"
name: "avg_pool"
Neck:
name: "VehicleNeck"
in_channels: 2048

18
ppcls/engine/engine.py
View File

@ -161,7 +161,7 @@ class Engine(object):
if metric_config is not None:
metric_config = metric_config.get("Train")
if metric_config is not None:
if self.train_dataloader.collate_fn:
if hasattr(self.train_dataloader, "collate_fn"):
for m_idx, m in enumerate(metric_config):
if "TopkAcc" in m:
msg = f"'TopkAcc' metric can not be used when setting 'batch_transform_ops' in config. The 'TopkAcc' metric has been removed."
@ -312,14 +312,14 @@ class Engine(object):
self.output_dir,
model_name=self.config["Arch"]["name"],
prefix="epoch_{}".format(epoch_id))
# save the latest model
save_load.save_model(
self.model,
self.optimizer, {"metric": acc,
"epoch": epoch_id},
self.output_dir,
model_name=self.config["Arch"]["name"],
prefix="latest")
# save the latest model
save_load.save_model(
self.model,
self.optimizer, {"metric": acc,
"epoch": epoch_id},
self.output_dir,
model_name=self.config["Arch"]["name"],
prefix="latest")
if self.vdl_writer is not None:
self.vdl_writer.close()

27
test_tipc/config/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5_train_custom_sampler.txt 100644
View File

@ -0,0 +1,27 @@
===========================train_params===========================
model_name:GeneralRecognition_PPLCNet_x2_5
python:python3.7
gpu_list:0|0,1
-o Global.device:gpu
-o Global.auto_cast:null
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
-o Global.output_dir:./output/
-o DataLoader.Train.sampler.batch_size:8
-o Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./dataset/ILSVRC2012/val
null:null
##
trainer:norm_train
norm_train:tools/train.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml -o DataLoader.Train.sampler.name="PKSampler" -o DataLoader.Train.sampler.sample_per_id=2
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:null
null:null
##

26
test_tipc/config/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5_train_multicard_eval.txt 100644
View File

@ -0,0 +1,26 @@
===========================train_params===========================
model_name:GeneralRecognition_PPLCNet_x2_5
python:python3.7
gpu_list:0,1
-o Global.device:gpu
-o Global.auto_cast:null
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
-o Global.output_dir:./output/
-o DataLoader.Train.sampler.batch_size:8
-o Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./dataset/ILSVRC2012/val
null:null
##
trainer:norm_train
norm_train:tools/train.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:null
null:null
##

26
test_tipc/config/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5_train_no_eval.txt 100644
View File

@ -0,0 +1,26 @@
===========================train_params===========================
model_name:GeneralRecognition_PPLCNet_x2_5
python:python3.7
gpu_list:0|0,1
-o Global.device:gpu
-o Global.auto_cast:null
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
-o Global.output_dir:./output/
-o DataLoader.Train.sampler.batch_size:8
-o Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./dataset/ILSVRC2012/val
null:null
##
trainer:norm_train
norm_train:tools/train.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.eval_during_train=False
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:null
null:null
##