WuHaobo
GitHub
commit
dab1feb0d4
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@ -0,0 +1,75 @@
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mode: 'train'
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ARCHITECTURE:
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name: 'RegNetX_4GF'
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pretrained_model: ""
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model_save_dir: "./output/"
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classes_num: 1000
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total_images: 1281167
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save_interval: 1
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validate: True
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valid_interval: 1
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epochs: 100
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topk: 5
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image_shape: [3, 224, 224]
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use_mix: False
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ls_epsilon: -1
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LEARNING_RATE:
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function: 'CosineWarmup'
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params:
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lr: 0.4
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warmup_epoch: 5
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OPTIMIZER:
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function: 'Momentum'
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params:
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momentum: 0.9
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regularizer:
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function: 'L2'
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factor: 0.000050
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TRAIN:
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batch_size: 512
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num_workers: 4
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file_list: "./dataset/ILSVRC2012/train_list.txt"
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data_dir: "./dataset/ILSVRC2012/"
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shuffle_seed: 0
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transforms:
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- DecodeImage:
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to_rgb: True
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to_np: False
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channel_first: False
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- RandCropImage:
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size: 224
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- RandFlipImage:
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flip_code: 1
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- NormalizeImage:
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scale: 1./255.
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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order: ''
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- ToCHWImage:
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VALID:
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batch_size: 256
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num_workers: 4
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file_list: "./dataset/ILSVRC2012/val_list.txt"
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data_dir: "./dataset/ILSVRC2012/"
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shuffle_seed: 0
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transforms:
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- DecodeImage:
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to_rgb: True
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to_np: False
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channel_first: False
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- ResizeImage:
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resize_short: 256
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- CropImage:
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size: 224
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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order: ''
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- ToCHWImage:
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@ -18,6 +18,7 @@ from .mobilenet_v2 import MobileNetV2_x0_25, MobileNetV2_x0_5, MobileNetV2_x0_75
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from .mobilenet_v3 import MobileNetV3_small_x0_35, MobileNetV3_small_x0_5, MobileNetV3_small_x0_75, MobileNetV3_small_x1_0, MobileNetV3_small_x1_25, MobileNetV3_large_x0_35, MobileNetV3_large_x0_5, MobileNetV3_large_x0_75, MobileNetV3_large_x1_0, MobileNetV3_large_x1_25
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from .googlenet import GoogLeNet
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from .vgg import VGG11, VGG13, VGG16, VGG19
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from .regnet import RegNetX_200MF, RegNetX_4GF, RegNetX_32GF, RegNetY_200MF, RegNetY_4GF, RegNetY_32GF
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from .resnet import ResNet18, ResNet34, ResNet50, ResNet101, ResNet152
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from .resnet_vc import ResNet50_vc, ResNet101_vc, ResNet152_vc
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from .resnet_vd import ResNet18_vd, ResNet34_vd, ResNet50_vd, ResNet101_vd, ResNet152_vd, ResNet200_vd
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@ -0,0 +1,356 @@
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# 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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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 math
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import numpy as np
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import paddle
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import paddle.fluid as fluid
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from paddle.fluid.param_attr import ParamAttr
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__all__ = [
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"RegNetX_200MF", "RegNetX_4GF", "RegNetX_32GF", "RegNetY_200MF",
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"RegNetY_4GF", "RegNetY_32GF"
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]
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class RegNet():
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def __init__(self, w_a, w_0, w_m, d, group_w, bot_mul, q=8, se_on=False):
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self.w_a = w_a
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self.w_0 = w_0
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self.w_m = w_m
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self.d = d
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self.q = q
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self.group_w = group_w
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self.bot_mul = bot_mul
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# Stem type
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self.stem_type = "simple_stem_in"
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# Stem width
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self.stem_w = 32
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# Block type
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self.block_type = "res_bottleneck_block"
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# Stride of each stage
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self.stride = 2
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# Squeeze-and-Excitation (RegNetY)
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self.se_on = se_on
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self.se_r = 0.25
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def quantize_float(self, 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(self, 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 = [
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self.quantize_float(w_bot, g) for w_bot, g in zip(ws_bot, gs)
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]
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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(self, 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(self, 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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def init_weights(self, op_type, filter_size=0, num_channels=0, name=None):
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if op_type == 'conv':
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fan_out = num_channels * filter_size * filter_size
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param_attr = ParamAttr(
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name=name + "_weights",
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initializer=fluid.initializer.NormalInitializer(
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loc=0.0, scale=math.sqrt(2.0 / fan_out)))
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bias_attr = False
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elif op_type == 'bn':
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param_attr = ParamAttr(
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name=name + "_scale",
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initializer=fluid.initializer.Constant(0.0))
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bias_attr = ParamAttr(
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name=name + "_offset",
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initializer=fluid.initializer.Constant(0.0))
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elif op_type == 'final_bn':
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param_attr = ParamAttr(
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name=name + "_scale",
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initializer=fluid.initializer.Constant(1.0))
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bias_attr = ParamAttr(
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name=name + "_offset",
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initializer=fluid.initializer.Constant(0.0))
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return param_attr, bias_attr
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def net(self, input, class_dim=1000):
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# Generate RegNet ws per block
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b_ws, num_s, max_s, ws_cont = self.generate_regnet(
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self.w_a, self.w_0, self.w_m, self.d, self.q)
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# Convert to per stage format
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ws, ds = self.get_stages_from_blocks(b_ws, b_ws)
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# Generate group widths and bot muls
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gws = [self.group_w for _ in range(num_s)]
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bms = [self.bot_mul for _ in range(num_s)]
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# Adjust the compatibility of ws and gws
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ws, gws = self.adjust_ws_gs_comp(ws, bms, gws)
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# Use the same stride for each stage
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ss = [self.stride for _ in range(num_s)]
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# Use SE for RegNetY
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se_r = self.se_r
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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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conv = self.conv_bn_layer(
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input=input,
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num_filters=self.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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# Construct the stages
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for block, (d, w_out, stride, bm, gw) in enumerate(stage_params):
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for i in range(d):
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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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conv = self.bottleneck_block(
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input=conv,
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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_r=self.se_r,
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name=conv_name)
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pool = fluid.layers.pool2d(
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input=conv, pool_type='avg', global_pooling=True)
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out = fluid.layers.fc(
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input=pool,
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size=class_dim,
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param_attr=ParamAttr(
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name="fc_0.w_0",
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initializer=fluid.initializer.NormalInitializer(
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loc=0.0, scale=0.01)),
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bias_attr=ParamAttr(
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name="fc_0.b_0", initializer=fluid.initializer.Constant(0.0)))
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return out
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def conv_bn_layer(self,
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input,
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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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final_bn=False):
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param_attr, bias_attr = self.init_weights(
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op_type='conv',
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filter_size=filter_size,
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num_channels=num_filters,
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name=name)
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conv = fluid.layers.conv2d(
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input=input,
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num_filters=num_filters,
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filter_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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act=None,
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param_attr=param_attr,
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bias_attr=bias_attr,
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name=name + '.conv2d.output.1')
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bn_name = name + '_bn'
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if final_bn:
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param_attr, bias_attr = self.init_weights(
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op_type='final_bn', name=bn_name)
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else:
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param_attr, bias_attr = self.init_weights(
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op_type='bn', name=bn_name)
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return fluid.layers.batch_norm(
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input=conv,
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act=act,
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name=bn_name + '.output.1',
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param_attr=param_attr,
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bias_attr=bias_attr,
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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 shortcut(self, input, ch_out, stride, name):
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ch_in = input.shape[1]
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if ch_in != ch_out or stride != 1:
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return self.conv_bn_layer(
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input=input,
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num_filters=ch_out,
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filter_size=1,
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stride=stride,
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padding=0,
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act=None,
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name=name)
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else:
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return input
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def bottleneck_block(self, input, num_filters, stride, bm, gw, se_r, name):
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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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conv0 = self.conv_bn_layer(
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input=input,
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num_filters=w_b,
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filter_size=1,
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stride=1,
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padding=0,
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act='relu',
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name=name + "_branch2a")
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conv1 = self.conv_bn_layer(
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input=conv0,
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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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# Squeeze-and-Excitation (SE)
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if self.se_on:
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w_se = int(round(input.shape[1] * se_r))
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conv1 = self.squeeze_excitation(
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input=conv1,
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num_channels=w_b,
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reduction_channels=w_se,
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name=name + "_branch2se")
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conv2 = self.conv_bn_layer(
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input=conv1,
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num_filters=num_filters,
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filter_size=1,
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stride=1,
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padding=0,
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act=None,
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name=name + "_branch2c",
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final_bn=True)
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short = self.shortcut(
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input, num_filters, stride, name=name + "_branch1")
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return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
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def squeeze_excitation(self,
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input,
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num_channels,
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reduction_channels,
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name=None):
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pool = fluid.layers.pool2d(
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input=input, pool_size=0, pool_type='avg', global_pooling=True)
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fan_out = num_channels
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squeeze = fluid.layers.conv2d(
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input=pool,
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num_filters=reduction_channels,
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filter_size=1,
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act='relu',
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param_attr=ParamAttr(
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initializer=fluid.initializer.NormalInitializer(
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loc=0.0, scale=math.sqrt(2.0 / fan_out)),
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name=name + '_sqz_weights'),
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bias_attr=ParamAttr(name=name + '_sqz_offset'))
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excitation = fluid.layers.conv2d(
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input=squeeze,
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num_filters=num_channels,
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filter_size=1,
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act='sigmoid',
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param_attr=ParamAttr(
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initializer=fluid.initializer.NormalInitializer(
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loc=0.0, scale=math.sqrt(2.0 / fan_out)),
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name=name + '_exc_weights'),
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bias_attr=ParamAttr(name=name + '_exc_offset'))
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scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
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return scale
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def RegNetX_200MF():
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model = RegNet(
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w_a=36.44, w_0=24, w_m=2.49, d=13, group_w=8, bot_mul=1.0, q=8)
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return model
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def RegNetX_4GF():
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model = RegNet(
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w_a=38.65, w_0=96, w_m=2.43, d=23, group_w=40, bot_mul=1.0, q=8)
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return model
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def RegNetX_32GF():
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model = RegNet(
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w_a=69.86, w_0=320, w_m=2.0, d=23, group_w=168, bot_mul=1.0, q=8)
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return model
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def RegNetY_200MF():
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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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return model
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def RegNetY_4GF():
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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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return model
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def RegNetY_32GF():
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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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return model
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Reference in New Issue