def mult_l(): text = [] data = pd.read_csv('D:/library/文本分类/train.csv', encoding='utf-8') tfidf = TfidfVectorizer(norm='l2', ngram_range=(1, 2)) features = tfidf.fit_transform(data.word) # print(tfidf.get_feature_names()) # print(features.toarray()) list_dir_test = os.listdir('D:/library/文本分类/test/') for t in list_dir_test: with open('D:/library/文本分类/test/'+t, 'r', encoding='utf-8') as f: format_sec = f.read() text.append(format_sec) features1 = tfidf.transform(text) clf = joblib.load( 'D:/library/fenci/MultinomialNB_classify.pkl') # 将模型存储在变量clf_load中 cat_id_df = data[['id', 'id_style']].drop_duplicates( ).sort_values('id_style').reset_index(drop=True) cat_to_id = dict(cat_id_df.values) id_to_cat = dict(cat_id_df[['id_style', 'id']].values) pred_cat_id = clf.predict(features1) # print(pred_cat_id) print('测试集文件：', 'D:/library/fenci/test_1/' + t, '预测类别：', id_to_cat[pred_cat_id[0]]) text.clear() if name == "main": mult_l()报错为ValueError: X has 326125 features, but MultinomialNB is expecting 59079 features as input.

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written_mult.asm.rar_Mult. asm_space

标题中的"written_mult.asm.rar_Mult.asm_space"表明这是一个关于汇编语言编程的资源，特别是与多重（Mult）操作和空间控制（space control）相关的。这个压缩包可能包含一个名为"Mult.asm"的源代码文件，该文件是用...

APP_ONENET_MQTT_MULT_TOPIC:MULT_TOPIC

标题 "APP_ONENET_MQTT_MULT_TOPIC:MULT_TOPIC" 提到的是一个关于使用MQTT协议在物联网（IoT）平台OneNet上的多主题应用。这个应用可能涉及到设备的远程控制、数据采集以及多设备间的数据交换。在物联网场景中，MQTT...

def load_file(path): data = pd.read_csv(path, sep=',') is_benign = data[' Label'] == 'BENIGN' flows_normal = data[is_benign] flows_abnormal_full = data[~is_benign] sizeDownSample = len(flows_normal) * mult if (len(flows_normal) * mult) < (len(flows_abnorm

代码首先用 pandas 库的 read_csv 函数读取 csv 文件，并且通过判断每条流量的标签是否为 BENIGN 来区分正常流量和异常流量。然后根据采样倍数 mult 和正常流量数量计算采样后的异常流量数量 sizeDownSample。...

model: base_learning_rate: 4.5e-6 target: ldm.models.autoencoder.AutoencoderKL params: monitor: "val/rec_loss" embed_dim: 4 lossconfig: target: ldm.modules.losses.LPIPSWithDiscriminator params: disc_start: 50001 kl_weight: 0.000001 disc_weight: 0.5 ddconfig: double_z: True z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: [ 1,2,4,4 ] # num_down = len(ch_mult)-1 num_res_blocks: 2 attn_resolutions: [ ] dropout: 0.0 data: target: main.DataModuleFromConfig params: batch_size: 12 wrap: True train: target: ldm.data.imagenet.ImageNetSRTrain params: size: 256 degradation: pil_nearest validation: target: ldm.data.imagenet.ImageNetSRValidation params: size: 256 degradation: pil_nearest lightning: callbacks: image_logger: target: main.ImageLogger params: batch_frequency: 1000 max_images: 8 increase_log_steps: True trainer: benchmark: True accumulate_grad_batches: 2 Footer © 2023 GitHub, Inc. Footer navigation Terms Privacy Security Status D怎么理解上述模型配置

上述模型配置是一个使用Lightning Distributed Module（LDM）框架训练的自编码器模型，其中包含了模型、数据、和Lightning的配置信息。在模型配置中，我们可以看到该自编码器模型使用了KL散度损失和...

我正在编辑【通达信条件选股】代码，遇到了【单词最大字符数不得超过 15 个错误起始位置 : 0 ; 长度: 0 】，请帮我检查并改正错误点补全正确代码，生成修正后完整代码。原有选股逻辑完整保留。我的原始代码如下：【// 智能选股系统 V8.3 OPT // █ 神经微分优化模块 PARAM_OPT := NEURAL_DIFFEVO( // 原名NEURAL_DIFFERENTIAL_EVOLUTION缩写 EPOCH := 3000, POP := 2000, MUT_RATE := ADAP_MUT(0.15,0.003), CROSS := TOP_CROSS(0.7), FITNESS := 0.7SHARPE + 0.3CALMAR, CONSTRAIN := [MAX_CHG<0.2, FEAT_IMP>0.05]); // █ 3D特征引擎 ALPHA_FACTOR := DEEP_FEAT_FUSE( T_STR := T_CONV( // T_STREAM缩写 [N_VOL(5,0.7), D_ORD(10,0.9)], // D_ORDER缩写 KERNEL := [3,5,7]), S_STR := SP_ATTN( // S_STREAM缩写 IND_CORR_MAT, SECT_EMB := 64), FUSE_LAYER := [ T_ENC(8,256), GBOOST_SEL(500,0.01)]); // █ 高频资金流 NORTH_FLOW := M_DRL( TIME_SCALE := [1M,5M,30M,1H], STATE_ENC := G_CONVNET( NODE_FEAT := [HKHOLD,IDX_FUT,ETF_FLOW], EDGE_W := X_CORR), REWARD := 1.4RET_SMTH(0.9) - 0.3VOL_DD + 0.2FLOW_GRAD); // █ 波动引擎 VOL_REGIME := N_REGIME( VOL_COMP := [VOL_BAND, GARCH(1,1), JUMP_DIFF], TRANS_NET := TCN( IN_DIM := 6, LEVELS := 8, DILATE := 2), JUMP_DET := B_CP( // JUMP_DETECT缩写 PRIOR_A := 0.1, WARN_TH := 0.95)); // █ 行业轮动 IND_SCORE := 0.35NLP_SENT( [NEWS,TWT,RED,INST_RSCH], // TWITTER缩写 T_DECAY := EXP(-0.07DELAY), TOPIC_CLU := B_TOPIC(256)) + 0.30ADAP_MOM( WINDOW := FOUR_AD(14), VOL_ADJ := TRUE, DECAY := 0.02) + 0.25FLOW_M3D( LEAD_LAG := [1.2, 0.8], LIQ_MULT := VOL_SMTH) + 0.10POLICY_SEN(2.0 - 0.35MKT_PHASE); // █ 信号融合 FINAL_SIGNAL := D_FUSION( INPUT := [ T_CONV(ALPHA_FACTOR, [3,5,7]), // 修正原ALPHA_STRM拼写 SP_ATTN(IND_CORR,64) ], FUSE_LAYER := [ T_ENC(8,256), GBOOST_SEL(500,0.01) ], ACT_COND := [ Q_SIG >= N_THRES(MKT_PH), VOL_RAT > 1.5*D_BETA, PRICE_Q(0.85,50), LIQ_SCORE>0.97, INST_FLOW>=3.2, ANOMALY_SC>NEURAL_ANO, FUND_CONF(3D,0.8) ], DYN_WEIGHT := N_WEIGHT(0.0005,20)); // █ 风控系统 RISK_CTRL := H_RISK( L1 := VOL_CAP(0.25,ADAP_SKEW), L2 := CORR_DIV( MAX_SECT := 0.15, MIN_DIVERG := 0.3), // MIN_DIVERGE缩写 L3 := BSWAN_PRO( STRESS_IDX>0.85, LIQ_FLOOR := 0.001, HEDGE := 0.3));】

STA_ENC := GCVNT(NF := [HKHD, IDXFUT, ETFFL], EW := XCRR), RWRD := 1.4*RTSM(0.9) - 0.3*VLDD + 0.2*FGDR); // █ 波动率模型 VL_RGME := NRGM(VLCMP := [VBLND, GRCH(1, 1), JDIF], TN_NET := TCNN(IN_DIM ...

class NLayerDiscriminator(nn.Module): def init(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True): super(NLayerDiscriminator, self).init() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d kw = 4 padw = int(np.ceil((kw-1)/2)) sequence = [ nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True) ] nf_mult = 1 for n in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2n, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2n_layers, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] if use_sigmoid: sequence += [nn.Sigmoid()] self.model = nn.Sequential(*sequence) def forward(self, input): return self.model(input)，逐行解释上述代码

这段代码实现了一个深度卷积神经网络的判别器，用于对输入的图像进行分类，判断其是否为真实图像。下面逐行解释代码： class NLayerDiscriminator(nn.Module): 定义了一个名为 NLayerDiscriminator 的类...

{———————— 智能选股系统 V8.3 OPT ————————} // █ 神经微分优化模块（修正重复定义） PARAM_OPT := NEURAL_DIFF_EVO( // 保留唯一定义位置 EPOCH := 3000, POP := 2000, MUT_RATE := ADAPT_MUT(0.15,0.003), CROSS := TOP_CROSS(0.7), FITNESS := 0.7SHARPE + 0.3CALMAR, CONSTRAIN := [MAX_CHG<0.2, FEAT_IMP>0.05]); // 补充分号 // █ 3D特征引擎（保持原有结构） ALPHA_FACTOR := DEEP_FEAT_FUSE( T_STREAM := TEMP_CONV( [NEURAL_VOL(5,0.7), DEEP_ORDER(10,0.9)], KERNEL := [3,5,7]), S_STREAM := SPAT_ATTN( IND_CORR_MAT, SECT_EMBED := 64), FUSE_LAYER := [ TRANS_ENC(8,256), GBOOST_SEL(500,0.01)]); // █ 高频资金流（保持原有参数） NORTH_FLOW := MULTI_DRL( TIME_SCALE := [1M,5M,30M,1H], STATE_ENC := G_CONV_NET( NODE_FEAT := [HKHOLD,IDX_FUT,ETF_FLOW], EDGE_W := CROSS_CORR), REWARD := 1.4RET_SMOOTH(0.9) - 0.3VOL_DD + 0.2FLOW_GRAD); // █ 波动引擎（语法验证通过） VOL_REGIME := NEURAL_REGIME( VOL_COMP := [VOL_BAND, GARCH(1,1), JUMP_DIFF], TRANS_NET := TCN( IN_DIM := 6, LEVELS := 8, DILATE := 2), JUMP_DETECT := BAYES_CP( PRIOR_A := 0.1, WARN_TH := 0.95)); // █ 行业轮动（系数校验无误） IND_SCORE := 0.35NLP_SENTIMENT( [NEWS,TWIT,RED,INST_RESEARCH], T_DECAY := EXP(-0.07DELAY), TOPIC_CLU := BERT_TOPIC(256)) + 0.30ADAPT_MOMENTUM( WINDOW := FOURIER_AD(14), VOL_ADJ := TRUE, DECAY := 0.02) + 0.25FLOW_MOM_3D( LEAD_LAG := [1.2, 0.8], LIQ_MULT := VOL_SMOOTH) + 0.10POLICY_SENSE(2.0 - 0.35MARKET_PHASE); // █ 信号融合（逻辑完整性保留） FINAL_SIGNAL := DEEP_FUSION( INPUT := [ TEMP_CONV(ALPHA_STREAM, [3,5,7]), SPAT_ATTN(IND_CORR,64) ], FUSE_LAYER := [ TRANS_ENC(8,256), GBOOST_SEL(500,0.01) ], ACT_COND := [ Q_SIG >= NEURAL_TH(MARKET_PH), VOL_RAT > 1.5*DYNA_BETA, PRICE_Q(0.85,50), LIQ_SCORE>0.97, INST_FLOW>=3.2, ANOM_SCORE>NEURAL_ANO, FUND_CONF(3D,0.8) ], DYN_WEIGHT := NEURAL_W(0.0005,20)); // █ 风控系统（约束条件完整） RISK_CTRL := HIER_RISK( L1 := VOL_CAP(0.25,ADAP_SKEW), L2 := CORR_DIV( MAX_SECT := 0.15, MIN_DIVERGE := 0.3), L3 := BLACKSWAN_PRO( STRESS_IDX>0.85, LIQ_FLOOR := 0.001, HEDGE := 0.3));你的身份是高级编程技术专家，精通各类编程语言，能对编程过程中的各类问题进行分析和解答。我的问题是【我正在编辑【通达信条件选股】代码，遇到了【错误句 : 详细信息 : 单词最大字符数不得超过 15 个错误起始位置 : 0 ; 长度: 0】，请帮我检查并改正错误点补全正确代码，生成修正后完整代码。原有选股逻辑完整保留。

LIQ_MULT := VOL_SMT) // SMOOTH→SMT + 0.10*POLICY_SENSE*(2.0 - 0.35*MARKET_PH); // █ 信号融合（逻辑保留） FINAL_SIGNAL := DEEP_FUSION( INPUT := [ TEMP_CONV(ALPHA_STREAM, [3,5,7]), SPAT_ATTN(IND...

Defines the PatchGAN discriminator with the specified arguments. class NLayerDiscriminator(nn.Module): def init(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True): super(NLayerDiscriminator, self).init() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d self.conv1 = nn.Conv2d(input_nc, ndf, kernel_size=3, padding=1) self.conv_offset1 = nn.Conv2d(ndf, 18, kernel_size=3, stride=1, padding=1) init_offset1 = torch.Tensor(np.zeros([18, ndf, 3, 3])) self.conv_offset1.weight = torch.nn.Parameter(init_offset1) # 初始化为0 self.conv_mask1 = nn.Conv2d(ndf, 9, kernel_size=3, stride=1, padding=1) init_mask1 = torch.Tensor(np.zeros([9, ndf, 3, 3]) + np.array([0.5])) self.conv_mask1.weight = torch.nn.Parameter(init_mask1) # 初始化为0.5 kw = 4 padw = int(np.ceil((kw-1)/2)) nf_mult = 1 for n in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2n, 8) self.sequence2 = [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2n_layers, 8) self.sequence2 += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] self.sequence2 += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] if use_sigmoid: self.sequence2 += [nn.Sigmoid()] def forward(self, input): input = self.conv1(input) offset1 = self.conv_offset1(input) mask1 = torch.sigmoid(self.conv_mask1(input)) sequence1 = [ torchvision.ops.deform_conv2d(input=input, offset=offset1, weight=self.conv1.weight, mask=mask1, padding=(1, 1)) 上述代码中出现错误：RuntimeError: Expected weight_c.size(1) * n_weight_grps == input_c.size(1) to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)，请问如何解决，给出修改后的代码

nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2**...

class NLayerDiscriminator(nn.Module): def init(self, input_channels=3, n_layers=4, use_sn=True): super().init() kernel_size = 4 padding = 1 sequence = [] # 初始卷积层 sequence += [ self._make_conv_block(input_channels, 64, kernel_size, 2, padding, use_sn, first_layer=True) ] self.output_shape = None # 中间卷积层 filter_mult = 1 for n in range(1, n_layers): filter_mult_prev = filter_mult filter_mult = min(2**n, 8) sequence += [ self._make_conv_block(64filter_mult_prev, 64filter_mult, kernel_size, 2, padding, use_sn) ] # 最终卷积层 filter_mult_prev = filter_mult sequence += [ self._make_conv_block(64filter_mult_prev, 64filter_mult, kernel_size, 1, padding, use_sn) ] # 输出层 sequence += [nn.Conv2d(64filter_mult, 1, kernel_size, 1, padding)] self.model = nn.Sequential(sequence) def _make_conv_block(self, in_c, out_c, kernel, stride, pad, use_sn, first_layer=False): layers = [] # 卷积层 conv = nn.Conv2d(in_c, out_c, kernel_size=kernel, stride=stride, padding=pad) if use_sn: conv = spectral_norm(conv) # 谱归一化 layers.append(conv) if not first_layer: layers.append(nn.InstanceNorm2d(out_c)) layers.append(nn.LeakyReLU(0.2, inplace=True)) return nn.Sequential(*layers) def forward(self, x): x = self.model(x) if self.output_shape is None: self.output_shape = x.shape[1:] return self.model(x) 报错： Given groups=1, weight of size [64, 3, 4, 4], expected input[1, 1, 14, 14] to have 3 channels, but got 1 channels instead

$$ \text{错误类型} = \frac{\text{预期输入通道数} - \text{实际输入通道数}}{\text{卷积核设计通道数}} \times 100\% $$ **根本原因分析：** 1. 模型第一个卷积层预设接收3通道输入（input_channels=3） 2. ...

model.py中：class MultiScaleDiscriminator(nn.Module): def init(self, input_channels=3, num_scales=3): super().init() self.num_scales = num_scales self.discriminators = nn.ModuleList() # 创建不同尺度的判别器 for i in range(num_scales): disc = NLayerDiscriminator( input_channels=input_channels, n_layers=4, use_sn=True # 启用谱归一化 ) self.discriminators.append(disc) # 下采样层用于多尺度处理 self.downsample = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False) def forward(self, x): outputs = [] for i in range(self.num_scales): # 逐级下采样输入图像 if i > 0: x = self.downsample(x) outputs.append(self.discriminators[i](x)) return outputs # 返回各尺度判别结果列表 class NLayerDiscriminator(nn.Module): def init(self, input_channels=3, n_layers=4, use_sn=True): super().init() kernel_size = 4 padding = 1 sequence = [] # 初始卷积层 sequence += [ self._make_conv_block(input_channels, 64, kernel_size, 2, padding, use_sn, first_layer=True) ] # 中间卷积层 filter_mult = 1 for n in range(1, n_layers): filter_mult_prev = filter_mult filter_mult = min(2**n, 8) sequence += [ self._make_conv_block(64filter_mult_prev, 64filter_mult, kernel_size, 2, padding, use_sn) ] # 最终卷积层 filter_mult_prev = filter_mult sequence += [ self._make_conv_block(64filter_mult_prev, 64filter_mult, kernel_size, 1, padding, use_sn) ] # 输出层 sequence += [nn.Conv2d(64filter_mult, 1, kernel_size, 1, padding)] self.model = nn.Sequential(sequence) def _make_conv_block(self, in_c, out_c, kernel, stride, pad, use_sn, first_layer=False): layers = [] # 卷积层 conv = nn.Conv2d(in_c, out_c, kernel_size=kernel, stride=stride, padding=pad) if use_sn: conv = spectral_norm(conv) # 谱归一化 layers.append(conv) if not first_layer: layers.append(nn.InstanceNorm2d(out_c)) layers.append(nn.LeakyReLU(0.2, inplace=True)) return nn.Sequential(layers) def forward(self, x): return self.model(x) train.py中：real_A = batch["A"].cuda() real_B = batch["B"].cuda() # real_A = Variable(batch["A"]).cuda() ## 真图像A # real_B = Variable(batch["B"]).cuda() ## 真图像B #触发前向传播以初始化output_shape with torch.no_grad(): _=D_A(real_A) ## 全真，全假的标签 valid = torch.ones( (real_A.size(0), D_A.output_shape), device=real_A.device,requires_grad=False ) fake = torch.zeros( (real_A.size(0), *D_A.output_shape), device=real_A.device,requires_grad=False ) 出现报错：'MultiScaleDiscriminator' object has no attribute 'output_shape' 怎么解决

错误出现在train.py中，当他们尝试使用D_A.output_shape时，发现这个属性不存在。我需要帮他们解决这个问题。首先，我需要理解代码的结构。用户提供的MultiScaleDiscriminator类在model.py中定义，而错误发生在...

如何将self.conv1 = nn.Conv2d(4 * num_filters, num_filters, kernel_size=3, padding=1) self.conv_offset1 = nn.Conv2d(512, 18, kernel_size=3, stride=1, padding=1) init_offset1 = torch.Tensor(np.zeros([18, 512, 3, 3])) self.conv_offset1.weight = torch.nn.Parameter(init_offset1) # 初始化为0 self.conv_mask1 = nn.Conv2d(512, 9, kernel_size=3, stride=1, padding=1) init_mask1 = torch.Tensor(np.zeros([9, 512, 3, 3]) + np.array([0.5])) self.conv_mask1.weight = torch.nn.Parameter(init_mask1) # 初始化为0.5 与torchvision.ops.deform_conv2d，加入到：class NLayerDiscriminator(nn.Module): def init(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True): super(NLayerDiscriminator, self).init() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d kw = 4 padw = int(np.ceil((kw-1)/2)) sequence = [ nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True) ] nf_mult = 1 for n in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2n, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2n_layers, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] if use_sigmoid: sequence += [nn.Sigmoid()] self.model = nn.Sequential(*sequence) def forward(self, input): return self.model(input)中，请给出修改后的代码

nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2...

C:\Users\Guo\.conda\envs\ts39\python.exe C:\Users\Guo\Desktop\xm\PyTorch-Classification-Trainer1\train.py torch version:2.6.0+cpu 0.8.2 ============================================================ config_file: configs/config.yaml distributed: False data_type: age_gender train_data: ['data/megaage_asian/train.txt', 'data/morph/train.txt'] test_data: ['data/megaage_asian/test.txt'] class_name: ['female', 'male'] gender_class: 2 gender_loss: CrossEntropyLoss age_class: 70 age_loss: L1Loss use_age_ld: True train_transform: train test_transform: val work_dir: work_space/ net_type: AE_mobilenet_v2 width_mult: 1.0 input_size: [112, 112] rgb_mean: [0.5, 0.5, 0.5] rgb_std: [0.5, 0.5, 0.5] batch_size: 64 lr: 0.01 optim_type: SGD momentum: 0.9 num_epochs: 150 num_warn_up: 5 num_workers: 8 weight_decay: 0.0005 scheduler: ExpLR milestones: [30, 80, 120] gpu_id: [0] log_freq: 50 progress: True pretrained: True finetune: False ============================================================ INFO: build_train_loader,input_size:[112, 112] load data:data/megaage_asian/train.txt have data:37563 Dataset have images:37563 load data:data/morph/train.txt have data:49096 Dataset have images:49096 class_count ：{51: 961, 19: 2224, 28: 2852, 26: 2325, 24: 2856, 18: 2388, 31: 1785, 43: 1980, 32: 1743, 44: 1884, 21: 2235, 48: 1067, 29: 1992, 23: 2663, 22: 2409, 16: 1971, 46: 1429, 40: 2126, 34: 2204, 1: 861, 27: 2586, 17: 2317, 52: 1409, 42: 2084, 39: 2184, 20: 2436, 5: 321, 33: 2110, 56: 619, 12: 467, 69: 185, 37: 2587, 57: 504, 25: 2295, 36: 2440, 67: 262, 38: 2478, 47: 1348, 35: 2565, 50: 694, 53: 638, 4: 356, 10: 297, 3: 445, 8: 286, 30: 2037, 58: 355, 9: 243, 6: 296, 61: 271, 49: 1037, 54: 536, 41: 2051, 15: 531, 55: 645, 45: 1344, 63: 119, 7: 297, 59: 208, 2: 530, 60: 318, 66: 252, 68: 193, 64: 212, 11: 267, 14: 344, 13: 272, 62: 170, 65: 229, 0: 34} balance_nums：{51: 1270, 19: 1425, 28: 1471, 26: 1433, 24: 1471, 18: 1438, 31: 1384, 43: 1403, 32: 1380, 44: 1394, 21: 1426, 48: 1289, 29: 1404,

好的，我现在需要处理用户关于PyTorch分类训练脚本配置和数据加载的问题，特别是使用AE_mobilenet_v2模型在age_gender数据集上，PyTorch版本是2.6.0+cpu。首先，我应该先理清用户的需求，确保覆盖所有必要的部分。 ...

_base_ = [ './configs/_base_/datasets/pascal_voc12.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py', './topformer_tiny.py' ] optimizer = dict(_delete_=True, type='AdamW', lr=0.00012, betas=(0.9, 0.999), weight_decay=0.01, paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.), 'head': dict(lr_mult=10.), 'norm': dict(decay_mult=0.)})) lr_config = dict(_delete_=True, policy='poly', warmup='linear', warmup_iters=1500, warmup_ratio=1e-6, power=1.0, min_lr=0.0, by_epoch=False) # By default, models are trained on 8 GPUs with 2 images per GPU data=dict(samples_per_gpu=2) find_unused_parameters=True 这是不是一段config的代码

- data：定义数据加载和处理的设置，如每个GPU上的样本数。 - find_unused_parameters：设置是否查找未使用的参数。通过修改这些配置项，可以对模型训练过程中的各种参数和设置进行调整和优化，以满足特定任务...

介绍一下下面的代码：class HS_PFN(nn.Module): def init(self, in_channels, out_channels, depth_mult=1.0, expansion=1.0, act='silu'): super().init() hidden_channels = int(out_channels * expansion) self.channel_attn = ChannelAttention(in_channels) self.conv1 = ConvNormLayer(in_channels, hidden_channels, 1, 1, act=act) self.conv2 = ConvNormLayer(in_channels, hidden_channels, 3, 1, padding=1, act=act) self.bottlenecks = nn.Sequential([ RepVggBlock(hidden_channels, hidden_channels, act=act) for _ in range(round(3 depth_mult)) ]) if hidden_channels != out_channels: self.conv3 = ConvNormLayer(hidden_channels, out_channels, 1, 1, act=act) else: self.conv3 = nn.Identity() def forward(self, x): x = self.channel_attn(x) x_1 = self.conv1(x) x_1 = self.bottlenecks(x_1) x_2 = self.conv2(x) return self.conv3(x_1 + x_2)

def __init__(self, in_ch, out_ch, kernel_size=3): super().__init__() self.conv = nn.Conv2d(in_ch, out_ch, kernel_size, padding=kernel_size//2) self.norm = nn.BatchNorm2d(out_ch) self.act = nn....

.text .globl bitrev_table_32bit bitrev_table_32bit: .set noreorder #/* constants / ONE_ITEM_TABLE_BYTESIZE: .set 2 #/ size of one item in seed table in bytes / ONE_ITEM_BYTESIZE: .set 8 #/ number of bytes of one complex item / ONE_ITEM_BYTESIZE_EXP: .set 3 #/ 2^ONE_ITEM_BYTESIZE_EXP = ONE_ITEM_BYTESIZE */ TABLE_MULT_FACTOR: .set ONE_ITEM_BYTESIZE 报错dc/sdc_kernel/math/LBF_BitRevTable32.s: Assembler messages: prod/sdc/sdc_kernel/math/LBF_BitRevTable32.s:4: error: expected comma after "noreorder" prod/sdc/sdc_kernel/math/LBF_BitRevTable32.s:5: error: expected symbol name

嗯，用户遇到了汇编代码中的语法错误，具体是关于'noreorder'和符号名的报错。他们提供的文件是LBF_BitRevTable32.s，涉及到bitrev_table_32bit的定义。首先，我需要回忆一下汇编语言中常见的语法结构，尤其是与MIPS...

给以下代码写注释，要求每行写一句：class CosineAnnealingWarmbootingLR: # cawb learning rate scheduler: given the warm booting steps, calculate the learning rate automatically def init(self, optimizer, epochs=0, eta_min=0.05, steps=[], step_scale=0.8, lf=None, batchs=0, warmup_epoch=0, epoch_scale=1.0): self.warmup_iters = batchs * warmup_epoch self.optimizer = optimizer self.eta_min = eta_min self.iters = -1 self.iters_batch = -1 self.base_lr = [group['lr'] for group in optimizer.param_groups] self.step_scale = step_scale steps.sort() self.steps = [warmup_epoch] + [i for i in steps if (i < epochs and i > warmup_epoch)] + [epochs] self.gap = 0 self.last_epoch = 0 self.lf = lf self.epoch_scale = epoch_scale # Initialize epochs and base learning rates for group in optimizer.param_groups: group.setdefault('initial_lr', group['lr']) def step(self, external_iter = None): self.iters += 1 if external_iter is not None: self.iters = external_iter # cos warm boot policy iters = self.iters + self.last_epoch scale = 1.0 for i in range(len(self.steps)-1): if (iters <= self.steps[i+1]): self.gap = self.steps[i+1] - self.steps[i] iters = iters - self.steps[i] if i != len(self.steps)-2: self.gap += self.epoch_scale break scale = self.step_scale if self.lf is None: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale lr * ((((1 + math.cos(iters * math.pi / self.gap)) / 2) ** 1.0) * (1.0 - self.eta_min) + self.eta_min) else: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale * lr * self.lf(iters, self.gap) return self.optimizer.param_groups[0]['lr'] def step_batch(self): self.iters_batch += 1 if self.iters_batch < self.warmup_iters: rate = self.iters_batch / self.warmup_iters for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = lr * rate return self.optimizer.param_groups[0]['lr'] else: return None

def __init__(self, optimizer, T_max, T_mult=1, eta_min=0, last_epoch=-1): 这是类的初始化函数，接受optimizer、T_max、T_mult、eta_min和last_epoch五个参数。 self.optimizer = optimizer self.T_max = T...

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