Spaces:
Runtime error
Runtime error
File size: 5,036 Bytes
3e99b05 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 |
# import torch
# import copy
# from torch import nn, Tensor
# import os
#
# import math
# import torch.nn.functional as F
# from torch import nn
#
#
# class MLP(nn.Module):
# """ Very simple multi-layer perceptron (also called FFN)"""
#
# def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
# super().__init__()
# self.num_layers = num_layers
# h = [hidden_dim] * (num_layers - 1)
# self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
#
# def forward(self, x):
# for i, layer in enumerate(self.layers):
# x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
# return x
#
#
# def inverse_sigmoid(x, eps=1e-5):
# x = x.clamp(min=0, max=1)
# x1 = x.clamp(min=eps)
# x2 = (1 - x).clamp(min=eps)
# return torch.log(x1/x2)
#
#
# def gen_encoder_output_proposals(memory:Tensor, memory_padding_mask:Tensor, spatial_shapes:Tensor):
# """
# Input:
# - memory: bs, \sum{hw}, d_model
# - memory_padding_mask: bs, \sum{hw}
# - spatial_shapes: nlevel, 2
# Output:
# - output_memory: bs, \sum{hw}, d_model
# - output_proposals: bs, \sum{hw}, 4
# """
# N_, S_, C_ = memory.shape
# base_scale = 4.0
# proposals = []
# _cur = 0
# for lvl, (H_, W_) in enumerate(spatial_shapes):
# mask_flatten_ = memory_padding_mask[:, _cur:(_cur + H_ * W_)].view(N_, H_, W_, 1)
# valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
# valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)
#
# grid_y, grid_x = torch.meshgrid(torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device),
# torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device))
# grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)
#
# scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2)
# grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
# wh = torch.ones_like(grid) * 0.05 * (2.0 ** lvl)
# proposal = torch.cat((grid, wh), -1).view(N_, -1, 4)
# proposals.append(proposal)
# _cur += (H_ * W_)
# output_proposals = torch.cat(proposals, 1)
# output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True)
# output_proposals = torch.log(output_proposals / (1 - output_proposals))
# output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf'))
# output_proposals = output_proposals.masked_fill(~output_proposals_valid, float('inf'))
#
# output_memory = memory
# output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0))
# output_memory = output_memory.masked_fill(~output_proposals_valid, float(0))
# return output_memory, output_proposals
#
#
# def gen_sineembed_for_position(pos_tensor):
# # n_query, bs, _ = pos_tensor.size()
# # sineembed_tensor = torch.zeros(n_query, bs, 256)
# scale = 2 * math.pi
# dim_t = torch.arange(128, dtype=torch.float32, device=pos_tensor.device)
# dim_t = 10000 ** (2 * (dim_t // 2) / 128)
# x_embed = pos_tensor[:, :, 0] * scale
# y_embed = pos_tensor[:, :, 1] * scale
# pos_x = x_embed[:, :, None] / dim_t
# pos_y = y_embed[:, :, None] / dim_t
# pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2)
# pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2)
# if pos_tensor.size(-1) == 2:
# pos = torch.cat((pos_y, pos_x), dim=2)
# elif pos_tensor.size(-1) == 4:
# w_embed = pos_tensor[:, :, 2] * scale
# pos_w = w_embed[:, :, None] / dim_t
# pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2)
#
# h_embed = pos_tensor[:, :, 3] * scale
# pos_h = h_embed[:, :, None] / dim_t
# pos_h = torch.stack((pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3).flatten(2)
#
# pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2)
# else:
# raise ValueError("Unknown pos_tensor shape(-1):{}".format(pos_tensor.size(-1)))
# return pos
#
#
# def _get_activation_fn(activation):
# """Return an activation function given a string"""
# if activation == "relu":
# return F.relu
# if activation == "gelu":
# return F.gelu
# if activation == "glu":
# return F.glu
# if activation == "prelu":
# return nn.PReLU()
# if activation == "selu":
# return F.selu
# raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
#
#
# def _get_clones(module, N, layer_share=False):
# # import ipdb; ipdb.set_trace()
# if layer_share:
# return nn.ModuleList([module for i in range(N)])
# else:
# return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) |