123 lines
4.7 KiB
Python
Executable File
123 lines
4.7 KiB
Python
Executable File
import torch
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import copy
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from torch import nn, Tensor
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import os
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import math
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import torch.nn.functional as F
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from torch import nn
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class MLP(nn.Module):
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""" Very simple multi-layer perceptron (also called FFN)"""
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def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
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super().__init__()
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self.num_layers = num_layers
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h = [hidden_dim] * (num_layers - 1)
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self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
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def forward(self, x):
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for i, layer in enumerate(self.layers):
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x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
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return x
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def inverse_sigmoid(x, eps=1e-5):
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x = x.clamp(min=0, max=1)
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x1 = x.clamp(min=eps)
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x2 = (1 - x).clamp(min=eps)
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return torch.log(x1/x2)
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def gen_encoder_output_proposals(memory:Tensor, memory_padding_mask:Tensor, spatial_shapes:Tensor):
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"""
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Input:
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- memory: bs, \sum{hw}, d_model
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- memory_padding_mask: bs, \sum{hw}
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- spatial_shapes: nlevel, 2
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Output:
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- output_memory: bs, \sum{hw}, d_model
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- output_proposals: bs, \sum{hw}, 4
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"""
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N_, S_, C_ = memory.shape
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base_scale = 4.0
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proposals = []
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_cur = 0
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for lvl, (H_, W_) in enumerate(spatial_shapes):
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mask_flatten_ = memory_padding_mask[:, _cur:(_cur + H_ * W_)].view(N_, H_, W_, 1)
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valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
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valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)
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grid_y, grid_x = torch.meshgrid(torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device),
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torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device))
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grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)
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scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2)
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grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
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wh = torch.ones_like(grid) * 0.05 * (2.0 ** lvl)
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proposal = torch.cat((grid, wh), -1).view(N_, -1, 4)
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proposals.append(proposal)
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_cur += (H_ * W_)
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output_proposals = torch.cat(proposals, 1)
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output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True)
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output_proposals = torch.log(output_proposals / (1 - output_proposals))
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output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf'))
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output_proposals = output_proposals.masked_fill(~output_proposals_valid, float('inf'))
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output_memory = memory
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output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0))
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output_memory = output_memory.masked_fill(~output_proposals_valid, float(0))
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return output_memory, output_proposals
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def gen_sineembed_for_position(pos_tensor, dim=128):
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# n_query, bs, _ = pos_tensor.size()
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# sineembed_tensor = torch.zeros(n_query, bs, 256)
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scale = 2 * math.pi
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dim_t = torch.arange(dim, dtype=torch.float32, device=pos_tensor.device)
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dim_t = 10000 ** (2 * (dim_t // 2) / dim)
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x_embed = pos_tensor[:, :, 0] * scale
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y_embed = pos_tensor[:, :, 1] * scale
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pos_x = x_embed[:, :, None] / dim_t
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pos_y = y_embed[:, :, None] / dim_t
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pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2)
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pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2)
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if pos_tensor.size(-1) == 2:
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pos = torch.cat((pos_y, pos_x), dim=2)
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elif pos_tensor.size(-1) == 4:
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w_embed = pos_tensor[:, :, 2] * scale
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pos_w = w_embed[:, :, None] / dim_t
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pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2)
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h_embed = pos_tensor[:, :, 3] * scale
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pos_h = h_embed[:, :, None] / dim_t
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pos_h = torch.stack((pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3).flatten(2)
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pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2)
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else:
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raise ValueError("Unknown pos_tensor shape(-1):{}".format(pos_tensor.size(-1)))
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return pos
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def _get_activation_fn(activation):
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"""Return an activation function given a string"""
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if activation == "relu":
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return F.relu
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if activation == "gelu":
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return F.gelu
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if activation == "glu":
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return F.glu
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if activation == "prelu":
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return nn.PReLU()
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if activation == "selu":
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return F.selu
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raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
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def _get_clones(module, N, layer_share=False):
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if layer_share:
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return nn.ModuleList([module for i in range(N)])
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else:
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return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) |