From e041ffca8df68afe2cb914416989079cfaf7faf9 Mon Sep 17 00:00:00 2001 From: jjyaoao <88936287+jjyaoao@users.noreply.github.com> Date: Tue, 11 Apr 2023 10:24:30 +0800 Subject: [PATCH] remove paddle/infrt/ (#52719) * remove paddle/infrt/ * delete .lit_test_times.txt --- .gitignore | 1 - .../tests/models/efficientnet-b4/net/utils.py | 424 ------------------ 2 files changed, 425 deletions(-) delete mode 100644 paddle/infrt/tests/models/efficientnet-b4/net/utils.py diff --git a/.gitignore b/.gitignore index 047d9684b4c..a4690ba4425 100644 --- a/.gitignore +++ b/.gitignore @@ -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 diff --git a/paddle/infrt/tests/models/efficientnet-b4/net/utils.py b/paddle/infrt/tests/models/efficientnet-b4/net/utils.py deleted file mode 100644 index 29c02c05842..00000000000 --- a/paddle/infrt/tests/models/efficientnet-b4/net/utils.py +++ /dev/null @@ -1,424 +0,0 @@ -# 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}') -- GitLab