Norway handball predictions today

Análise Comparativa entre Times Locais e Visitantes

Analisar as diferenças entre equipes locais e visitantes pode fornecer insights valiosos sobre possíveis vantagens ou desvantagens antes mesmo do início da partida.

Vantagens dos Times Locais

• Familiaridade com o Local:

Jogar em casa proporciona aos atletas familiaridade com as condições específicas do local como iluminação, dimensões do campo etc., o que pode influenciar positivamente seu desempenho físico e tático.

• Fisiológico: # Copyright (c) Facebook, Inc. and its affiliates. import numpy as np import torch from torch import nn from torch.nn import functional as F from detectron2.layers import ShapeSpec from detectron2.modeling import Backbone from detectron2.modeling.backbone.resnet import ( BasicStem, build_res_layer, ) from detectron2.modeling.backbone.resnet import ( DeformConv, ModulatedDeformConv, ) __all__ = ["build_resnet_backbone"] def build_resnet_backbone(cfg, input_shape): """ Create a ResNet instance from config. Returns: ResNet: a :class:`ResNet` instance. """ # need registration of new blocks/stems? norm = cfg.MODEL.RESNETS.NORM stem = BasicStem( in_channels=input_shape.channels, out_channels=cfg.MODEL.RESNETS.STEM_OUT_CHANNELS, norm=norm, input_size=input_shape.size[-2:], name="stem", ) freeze_at = cfg.MODEL.BACKBONE.FREEZE_AT if cfg.MODEL.RESNETS.DEFORM_ON_PER_STAGE: deform_on_per_stage = cfg.MODEL.RESNETS.DEFORM_ON_PER_STAGE assert isinstance(deform_on_per_stage, (list)) and len(deform_on_per_stage) == cfg.MODEL.RESNETS.NORM_FUNC_STAGE_ID[1], "deform_on_per_stage should be a list of bools of length {}.".format(cfg.MODEL.RESNETS.NORM_FUNC_STAGE_ID[1]) deform_on = [False] * cfg.MODEL.RESNETS.NORM_FUNC_STAGE_ID[1] for idx in range(len(deform_on_per_stage)): deform_on[cfg.MODEL.RESNETS.NORM_FUNC_STAGE_ID[1] - len(deform_on_per_stage) + idx] = deform_on_per_stage[idx] assert cfg.MODEL.RESNETS.DEFORM_MODULATED == False assert cfg.MODEL.RESNETS.DILATION == False assert cfg.MODEL.RESNETS.RES5_DILATION == False assert cfg.MODEL.RESNETS.RES5_DILATION == False assert cfg.MODEL.RESNETS.DEFORM_CFG is None num_groups = cfg.MODEL.RESNETS.NUM_GROUPS width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP bottleneck_channels = num_groups * width_per_group res5_dilation = cfg.MODEL.RESNETS.RES5_DILATION # Avoid creating variables without gradients # It consumes extra memory and may cause allreduce to fail out_features = cfg.MODEL.RESNETS.OUT_FEATURES if cfg.MODEL.META_ARCHITECTURE not in ["FCOS", "ATSS", "ATSS_CV"]: assert len(cfg.MODEL.RESNETS.EXTRA_CONV_LEVELS) <= len(out_features), "Cannot output more levels than layers {}".format(len(out_features)) stride_in_1x1 = cfg.MODEL.RESNETS.STRIDE_IN_1X1 num_blocks_per_stage = { "res2": cfg.MODEL.RESNETS.NUM_BLOCKS[0], "res3": cfg.MODEL.RESNETS.NUM_BLOCKS[1], "res4": cfg.MODEL.RESNETS.NUM_BLOCKS[2], "res5": cfg.MODEL.RESNETS.NUM_BLOCKS[3], } if res5_dilation == 1: num_blocks_per_stage["res5"] = num_blocks_per_stage["res5"] + len(cfg.MODEL.RESNETS.EXTRA_CONV_LEVELS) res_spec = [] in_channels = stem.out_channels for stage_idx, stage_name in enumerate(cfg.MODEL.RESNETS.STAGES): stage_blocks = num_blocks_per_stage[stage_name] dilation = res5_dilation if stage_idx >= res5_dilation else 1 first_stride = ( dilation if stage_idx == res5_dilation else cfg.MODEL.RESNETS.STAGE_SCALES[str(stage_idx)] ) # Avoid creating layers that are not used; this consumes extra memory # and may cause allreduce to fail. if out_features and (stage_name not in out_features): continue for block_idx in range(stage_blocks): block_dilation = dilation if block_idx >= stage_blocks - cfg.MODEL.RESNETS.DILATION_BLOCK_NUM else 1 # Extra conv levels with stride=2 are applied after the last block in each stage. stride_in_1x1_spatial = stride_in_1x1 if block_idx != stage_blocks - len(cfg.MODEL.RESNETS.EXTRA_CONV_LEVELS) else (1 if dilation > first_stride else first_stride) if block_idx == stage_blocks - len(cfg.MODEL.RESNETS.EXTRA_CONV_LEVELS): stride_in_1x1_temporal = stride_in_1x1_spatial / first_stride if dilation > first_stride else stride_in_1x1_spatial stride_in_1x1_spatial *= first_stride blocks_args = { "name": "{}{}".format(stage_name, block_idx + 1), "num_filters": bottleneck_channels, "stride_in_1x1_spatial": stride_in_1x1_spatial, "stride_in_1x1_temporal": stride_in_1x1_temporal if 'stride_in_1x1_temporal' in locals() else None, "groups": num_groups, "base_width": width_per_group, "input_channels": in_channels, "input_shape": ShapeSpec( channels=in_channels, height=stem.out_shape().height // (cfg.DATALOADER.SIZE_DIVISIBILITY ** max(stage_idx + int(dilation != first_stride), stem.spatial_dims)), width=stem.out_shape().width // (cfg.DATALOADER.SIZE_DIVISIBILITY ** max(stage_idx + int(dilation != first_stride), stem.spatial_dims)), depth=stem.out_shape().depth // (cfg.DATALOADER.SIZE_DIVISIBILITY ** max(stage_idx + int(dilation != first_stride), stem.spatial_dims)), ), "dilation": block_dilation, "norm": norm, "activation": cfg.MODEL.ANCHOR_FREE.ACTIVATION_FN, } res_spec.append(blocks_args) # TODO: add support for non-resnet backbones. blocks_args["block_class"] = DeformBottleneckBlock blocks_args["deform_modulated"] = False in_channels = blocks_args["num_filters"] return ResNet(stem, res_spec) # TODO: support more backbones with non-ResNet blocks. class ResNet(Backbone): """ Most ResNets are instantiated by :func:`build_resnet_backbone`.