433 lines
13 KiB
Python
433 lines
13 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Code was based on https://github.com/facebookresearch/pycls
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# reference: https://arxiv.org/abs/1905.13214
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import paddle
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from paddle import ParamAttr
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
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from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
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from paddle.nn.initializer import Uniform
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import math
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from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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MODEL_URLS = {
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"RegNetX_200MF":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_200MF_pretrained.pdparams",
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"RegNetX_4GF":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_4GF_pretrained.pdparams",
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"RegNetX_32GF":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_32GF_pretrained.pdparams",
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"RegNetY_200MF":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetY_200MF_pretrained.pdparams",
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"RegNetY_4GF":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetY_4GF_pretrained.pdparams",
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"RegNetY_32GF":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetY_32GF_pretrained.pdparams",
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}
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__all__ = list(MODEL_URLS.keys())
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def quantize_float(f, q):
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"""Converts a float to closest non-zero int divisible by q."""
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return int(round(f / q) * q)
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def adjust_ws_gs_comp(ws, bms, gs):
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"""Adjusts the compatibility of widths and groups."""
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ws_bot = [int(w * b) for w, b in zip(ws, bms)]
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gs = [min(g, w_bot) for g, w_bot in zip(gs, ws_bot)]
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ws_bot = [quantize_float(w_bot, g) for w_bot, g in zip(ws_bot, gs)]
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ws = [int(w_bot / b) for w_bot, b in zip(ws_bot, bms)]
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return ws, gs
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def get_stages_from_blocks(ws, rs):
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"""Gets ws/ds of network at each stage from per block values."""
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ts = [
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w != wp or r != rp
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for w, wp, r, rp in zip(ws + [0], [0] + ws, rs + [0], [0] + rs)
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]
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s_ws = [w for w, t in zip(ws, ts[:-1]) if t]
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s_ds = np.diff([d for d, t in zip(range(len(ts)), ts) if t]).tolist()
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return s_ws, s_ds
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def generate_regnet(w_a, w_0, w_m, d, q=8):
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"""Generates per block ws from RegNet parameters."""
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assert w_a >= 0 and w_0 > 0 and w_m > 1 and w_0 % q == 0
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ws_cont = np.arange(d) * w_a + w_0
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ks = np.round(np.log(ws_cont / w_0) / np.log(w_m))
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ws = w_0 * np.power(w_m, ks)
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ws = np.round(np.divide(ws, q)) * q
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num_stages, max_stage = len(np.unique(ws)), ks.max() + 1
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ws, ws_cont = ws.astype(int).tolist(), ws_cont.tolist()
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return ws, num_stages, max_stage, ws_cont
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class ConvBNLayer(nn.Layer):
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def __init__(self,
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num_channels,
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num_filters,
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filter_size,
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stride=1,
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groups=1,
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padding=0,
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act=None,
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name=None):
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super(ConvBNLayer, self).__init__()
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self._conv = Conv2D(
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in_channels=num_channels,
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out_channels=num_filters,
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kernel_size=filter_size,
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stride=stride,
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padding=padding,
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groups=groups,
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weight_attr=ParamAttr(name=name + ".conv2d.output.1.w_0"),
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bias_attr=ParamAttr(name=name + ".conv2d.output.1.b_0"))
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bn_name = name + "_bn"
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self._batch_norm = BatchNorm(
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num_filters,
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act=act,
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param_attr=ParamAttr(name=bn_name + ".output.1.w_0"),
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bias_attr=ParamAttr(bn_name + ".output.1.b_0"),
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moving_mean_name=bn_name + "_mean",
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moving_variance_name=bn_name + "_variance")
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def forward(self, inputs):
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y = self._conv(inputs)
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y = self._batch_norm(y)
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return y
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class BottleneckBlock(nn.Layer):
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def __init__(self,
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num_channels,
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num_filters,
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stride,
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bm,
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gw,
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se_on,
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se_r,
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shortcut=True,
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name=None):
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super(BottleneckBlock, self).__init__()
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# Compute the bottleneck width
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w_b = int(round(num_filters * bm))
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# Compute the number of groups
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num_gs = w_b // gw
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self.se_on = se_on
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self.conv0 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=w_b,
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filter_size=1,
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padding=0,
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act="relu",
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name=name + "_branch2a")
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self.conv1 = ConvBNLayer(
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num_channels=w_b,
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num_filters=w_b,
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filter_size=3,
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stride=stride,
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padding=1,
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groups=num_gs,
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act="relu",
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name=name + "_branch2b")
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if se_on:
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w_se = int(round(num_channels * se_r))
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self.se_block = SELayer(
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num_channels=w_b,
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num_filters=w_b,
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reduction_ratio=w_se,
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name=name + "_branch2se")
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self.conv2 = ConvBNLayer(
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num_channels=w_b,
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num_filters=num_filters,
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filter_size=1,
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act=None,
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name=name + "_branch2c")
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if not shortcut:
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self.short = ConvBNLayer(
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num_channels=num_channels,
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num_filters=num_filters,
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filter_size=1,
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stride=stride,
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name=name + "_branch1")
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self.shortcut = shortcut
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def forward(self, inputs):
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y = self.conv0(inputs)
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conv1 = self.conv1(y)
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if self.se_on:
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conv1 = self.se_block(conv1)
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conv2 = self.conv2(conv1)
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if self.shortcut:
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short = inputs
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else:
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short = self.short(inputs)
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y = paddle.add(x=short, y=conv2)
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y = F.relu(y)
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return y
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class SELayer(nn.Layer):
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def __init__(self, num_channels, num_filters, reduction_ratio, name=None):
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super(SELayer, self).__init__()
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self.pool2d_gap = AdaptiveAvgPool2D(1)
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self._num_channels = num_channels
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med_ch = int(num_channels / reduction_ratio)
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stdv = 1.0 / math.sqrt(num_channels * 1.0)
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self.squeeze = Linear(
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num_channels,
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med_ch,
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weight_attr=ParamAttr(
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initializer=Uniform(-stdv, stdv), name=name + "_sqz_weights"),
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bias_attr=ParamAttr(name=name + "_sqz_offset"))
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stdv = 1.0 / math.sqrt(med_ch * 1.0)
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self.excitation = Linear(
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med_ch,
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num_filters,
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weight_attr=ParamAttr(
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initializer=Uniform(-stdv, stdv), name=name + "_exc_weights"),
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bias_attr=ParamAttr(name=name + "_exc_offset"))
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def forward(self, input):
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pool = self.pool2d_gap(input)
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pool = paddle.reshape(pool, shape=[-1, self._num_channels])
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squeeze = self.squeeze(pool)
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squeeze = F.relu(squeeze)
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excitation = self.excitation(squeeze)
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excitation = F.sigmoid(excitation)
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excitation = paddle.reshape(
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excitation, shape=[-1, self._num_channels, 1, 1])
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out = input * excitation
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return out
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class RegNet(nn.Layer):
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def __init__(self,
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w_a,
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w_0,
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w_m,
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d,
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group_w,
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bot_mul,
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q=8,
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se_on=False,
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class_num=1000):
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super(RegNet, self).__init__()
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# Generate RegNet ws per block
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b_ws, num_s, max_s, ws_cont = generate_regnet(w_a, w_0, w_m, d, q)
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# Convert to per stage format
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ws, ds = get_stages_from_blocks(b_ws, b_ws)
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# Generate group widths and bot muls
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gws = [group_w for _ in range(num_s)]
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bms = [bot_mul for _ in range(num_s)]
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# Adjust the compatibility of ws and gws
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ws, gws = adjust_ws_gs_comp(ws, bms, gws)
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# Use the same stride for each stage
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ss = [2 for _ in range(num_s)]
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# Use SE for RegNetY
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se_r = 0.25
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# Construct the model
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# Group params by stage
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stage_params = list(zip(ds, ws, ss, bms, gws))
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# Construct the stem
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stem_type = "simple_stem_in"
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stem_w = 32
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block_type = "res_bottleneck_block"
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self.conv = ConvBNLayer(
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num_channels=3,
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num_filters=stem_w,
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filter_size=3,
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stride=2,
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padding=1,
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act="relu",
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name="stem_conv")
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self.block_list = []
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for block, (d, w_out, stride, bm, gw) in enumerate(stage_params):
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shortcut = False
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for i in range(d):
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num_channels = stem_w if block == i == 0 else in_channels
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# Stride apply to the first block of the stage
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b_stride = stride if i == 0 else 1
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conv_name = "s" + str(block + 1) + "_b" + str(i +
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1) # chr(97 + i)
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bottleneck_block = self.add_sublayer(
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conv_name,
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BottleneckBlock(
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num_channels=num_channels,
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num_filters=w_out,
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stride=b_stride,
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bm=bm,
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gw=gw,
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se_on=se_on,
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se_r=se_r,
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shortcut=shortcut,
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name=conv_name))
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in_channels = w_out
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self.block_list.append(bottleneck_block)
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shortcut = True
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self.pool2d_avg = AdaptiveAvgPool2D(1)
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self.pool2d_avg_channels = w_out
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stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)
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self.out = Linear(
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self.pool2d_avg_channels,
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class_num,
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weight_attr=ParamAttr(
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initializer=Uniform(-stdv, stdv), name="fc_0.w_0"),
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bias_attr=ParamAttr(name="fc_0.b_0"))
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def forward(self, inputs):
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y = self.conv(inputs)
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for block in self.block_list:
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y = block(y)
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y = self.pool2d_avg(y)
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y = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels])
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y = self.out(y)
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return y
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def _load_pretrained(pretrained, model, model_url, use_ssld=False):
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if pretrained is False:
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pass
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elif pretrained is True:
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load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
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elif isinstance(pretrained, str):
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load_dygraph_pretrain(model, pretrained)
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else:
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raise RuntimeError(
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"pretrained type is not available. Please use `string` or `boolean` type."
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)
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def RegNetX_200MF(pretrained=False, use_ssld=False, **kwargs):
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model = RegNet(
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w_a=36.44,
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w_0=24,
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w_m=2.49,
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d=13,
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group_w=8,
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bot_mul=1.0,
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q=8,
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**kwargs)
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_load_pretrained(
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pretrained, model, MODEL_URLS["RegNetX_200MF"], use_ssld=use_ssld)
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return model
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def RegNetX_4GF(pretrained=False, use_ssld=False, **kwargs):
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model = RegNet(
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w_a=38.65,
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w_0=96,
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w_m=2.43,
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d=23,
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group_w=40,
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bot_mul=1.0,
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q=8,
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**kwargs)
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_load_pretrained(
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pretrained, model, MODEL_URLS["RegNetX_4GF"], use_ssld=use_ssld)
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return model
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def RegNetX_32GF(pretrained=False, use_ssld=False, **kwargs):
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model = RegNet(
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w_a=69.86,
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w_0=320,
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w_m=2.0,
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d=23,
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group_w=168,
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bot_mul=1.0,
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q=8,
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**kwargs)
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_load_pretrained(
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pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
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return model
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def RegNetY_200MF(pretrained=False, use_ssld=False, **kwargs):
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model = RegNet(
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w_a=36.44,
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w_0=24,
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w_m=2.49,
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d=13,
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group_w=8,
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bot_mul=1.0,
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q=8,
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se_on=True,
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**kwargs)
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_load_pretrained(
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pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
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return model
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def RegNetY_4GF(pretrained=False, use_ssld=False, **kwargs):
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model = RegNet(
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w_a=31.41,
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w_0=96,
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w_m=2.24,
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d=22,
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group_w=64,
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bot_mul=1.0,
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q=8,
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se_on=True,
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**kwargs)
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_load_pretrained(
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pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
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return model
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def RegNetY_32GF(pretrained=False, use_ssld=False, **kwargs):
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model = RegNet(
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w_a=115.89,
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w_0=232,
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w_m=2.53,
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d=20,
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group_w=232,
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bot_mul=1.0,
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q=8,
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se_on=True,
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**kwargs)
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_load_pretrained(
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pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
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return model
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