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.

mult8_csdn.rar_ mult8_csdn_8位乘法器_binary multiplier_site:www.pudn

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...

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...

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**...

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...

_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...

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，接受五个参数：优化器optimizer、最大迭代次数T_max、迭代次数的倍数T_mult、学习率下限eta_min、上一次迭代的...

解释每一句super(CustomCSPPAN, self).init() out_channels = [max(round(c * width_mult), 1) for c in out_channels] block_num = max(round(block_num * depth_mult), 1) act = get_act_fn( act, trt=trt) if act is None or isinstance(act, (str, dict)) else act self.num_blocks = len(in_channels) self.data_format = data_format self._out_channels = out_channels in_channels = in_channels[::-1] fpn_stages = [] fpn_routes = [] for i, (ch_in, ch_out) in enumerate(zip(in_channels, out_channels)): if i > 0: ch_in += ch_pre // 2 stage = nn.Sequential() for j in range(stage_num): stage.add_sublayer( str(j), eval(stage_fn)(block_fn, ch_in if j == 0 else ch_out, ch_out, block_num, act=act, spp=(spp and i == 0))) if drop_block: stage.add_sublayer('drop', DropBlock(block_size, keep_prob))

这段代码是一个类的初始化方法。它首先调用了父类的初始化方法。然后，根据给定的参数计算了一些输出通道数和块的数量，以及激活函数。接下来，它计算了输入通道和输出通道的反向列表，然后创建了一个空的列表fpn_...

optimizer = dict(type='AdamW', lr=1e-3, betas=(0.9, 0.999), weight_decay=0.05, paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.), 'relative_position_bias_table': dict(decay_mult=0.), # 'temporal_position_bias_table': dict(decay_mult=0.), 'norm': dict(decay_mult=0.), 'backbone': dict(lr_mult=0.1)}))

这是一个优化器的配置字典，用于训练模型时控制参数优化的方式。这里使用的优化器是 AdamW，学习率为 1e-3，动量参数为 (0.9, 0.999)，权重衰减为 0.05。同时，通过 paramwise_cfg 字典中的 custom_keys 参数，对...

优化这段代码：class Point: def init(self , x = None , y = None): self.x = x self.y = y def repr(self): if self.x is None and self.y is None: return "Point(无穷远点)" else: return "Point({},{})".format(self.x,self.y) def point_add(P,Q): if P.x is None and P.y is None: return Q elif Q.x is None and Q.y is None: return P elif P.x == Q.x and P.y != Q.y: return Point(None,None) else: ld = 0 if P.x == Q.x and P.y == Q.y: ld = ((3 * P.x**2 + a) * pow(2 * P.y,-1,p)) % p else: ld = ((Q.y - P.y) * pow(Q.x - P.x, -1, p)) % p x3 = (ld**2 - P.x - Q.x) % p y3 = (ld * (P.x - x3) - P.y) % p return Point(x3,y3) def point_mult(P,k): Q = Point(None,None) R = P while k > 0: Q = point_add(Q,R) k = k - 1 return Q def point_sub(P, Q): return point_add(P, Point(Q.x, (-Q.y) % p)) print("请输入椭圆曲线的数值a、b和模值p：") a = input("a = ") b = input("b = ") p = int(input("p = ")) x1 = input("x1 = ") y1 = input("y1 = ") P = Point(x1,y1) x2 = input("x2 = ") y2 = input("y2 = ") Q = Point(x2,y2) print("P = ",P) print("Q = ",Q) print("P + Q =", point_add(P, Q)) print("2P =", point_mult(P, 2)) print("P - Q =", point_sub(P, Q))

def __init__(self, x=None, y=None): self.x = x self.y = y def __repr__(self): return "Point(无穷远点)" if self.x is None and self.y is None else f"Point({self.x},{self.y})" def __eq__(self, ...

assign I_mult_shift_signed = {14{I_mult_out[23]},I_mult_shift[9:0]}; 的语法错误

assign I_mult_shift_signed = {14{I_mult_out[23]},I_mult_shift[9:0]}; 这里有几点需要注意： 1. **位拼接的语法**：在Verilog中，使用大括号{}进行位拼接，但是其后必须跟着括号内的数字或表达式。在你的...

解释每一句 def init(self, layers=[3, 6, 6, 3], channels=[64, 128, 256, 512, 1024], act='swish', return_idx=[0, 1, 2, 3, 4], depth_wise=False, use_large_stem=False, width_mult=1.0, depth_mult=1.0, trt=False): super(CSPResNet, self).init() channels = [max(round(c * width_mult), 1) for c in channels] layers = [max(round(l * depth_mult), 1) for l in layers] act = get_act_fn( act, trt=trt) if act is None or isinstance(act, (str, dict)) else act

- width_mult：一个浮点数，表示 CSPResNet 模型中每个 CSPBlock 的通道数缩放因子。默认值为 1.0，表示不进行通道数缩放。 - depth_mult：一个浮点数，表示 CSPResNet 模型中每个 CSPBlock 的残差块数量缩放...

# building first layer input_channel = _make_divisible(input_channel * width_mult, round_nearest) self.out_channels = _make_divisible(last_channel * max(1.0, width_mult), round_nearest) features = [ConvBNReLU(3, input_channel, stride=2)] # building inverted residual blocks for t, c, n, s in self.cfgs: output_channel = _make_divisible(c * width_mult, round_nearest) for i in range(n): stride = s if i == 0 else 1 features.append(block(input_channel, output_channel, stride, expand_ratio=t)) input_channel = output_channel # building last several layers features.append(ConvBNReLU(input_channel, self.out_channels, kernel_size=1)) # make it nn.CellList self.features = nn.SequentialCell(features) self._initialize_weights()

首先，通过_make_divisible函数将输入通道数input_channel与缩放因子width_mult相乘并取整，得到经过缩放后的输入通道数。然后，通过_make_divisible函数将最后输出通道数last_channel与缩放因子width_mult相乘并...

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