269 lines
9.9 KiB
Python
269 lines
9.9 KiB
Python
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# ------------------------------------------------------------------------
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# Grounding DINO
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# url: https://github.com/IDEA-Research/GroundingDINO
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# Copyright (c) 2023 IDEA. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
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# ------------------------------------------------------------------------
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import copy
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import math
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import torch
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import torch.nn.functional as F
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from torch import Tensor, nn
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def _get_clones(module, N, layer_share=False):
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# import ipdb; ipdb.set_trace()
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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)])
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def get_sine_pos_embed(
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pos_tensor: torch.Tensor,
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num_pos_feats: int = 128,
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temperature: int = 10000,
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exchange_xy: bool = True,
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):
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"""generate sine position embedding from a position tensor
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Args:
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pos_tensor (torch.Tensor): shape: [..., n].
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num_pos_feats (int): projected shape for each float in the tensor.
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temperature (int): temperature in the sine/cosine function.
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exchange_xy (bool, optional): exchange pos x and pos y. \
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For example, input tensor is [x,y], the results will be [pos(y), pos(x)]. Defaults to True.
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Returns:
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pos_embed (torch.Tensor): shape: [..., n*num_pos_feats].
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"""
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scale = 2 * math.pi
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dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=pos_tensor.device)
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dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats)
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def sine_func(x: torch.Tensor):
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sin_x = x * scale / dim_t
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sin_x = torch.stack((sin_x[..., 0::2].sin(), sin_x[..., 1::2].cos()), dim=3).flatten(2)
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return sin_x
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pos_res = [sine_func(x) for x in pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)]
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if exchange_xy:
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pos_res[0], pos_res[1] = pos_res[1], pos_res[0]
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pos_res = torch.cat(pos_res, dim=-1)
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return pos_res
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def gen_encoder_output_proposals(
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memory: Tensor, memory_padding_mask: Tensor, spatial_shapes: Tensor, learnedwh=None
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):
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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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- learnedwh: 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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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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# import ipdb; ipdb.set_trace()
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grid_y, grid_x = torch.meshgrid(
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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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)
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grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) # H_, W_, 2
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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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if learnedwh is not None:
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# import ipdb; ipdb.set_trace()
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wh = torch.ones_like(grid) * learnedwh.sigmoid() * (2.0**lvl)
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else:
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wh = torch.ones_like(grid) * 0.05 * (2.0**lvl)
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# scale = torch.cat([W_[None].unsqueeze(-1), H_[None].unsqueeze(-1)], 1).view(1, 1, 1, 2).repeat(N_, 1, 1, 1)
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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) / scale
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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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# import ipdb; ipdb.set_trace()
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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(
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-1, keepdim=True
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)
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output_proposals = torch.log(output_proposals / (1 - output_proposals)) # unsigmoid
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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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# output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf'))
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# output_memory = output_memory.masked_fill(~output_proposals_valid, float('inf'))
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return output_memory, output_proposals
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class RandomBoxPerturber:
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def __init__(
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self, x_noise_scale=0.2, y_noise_scale=0.2, w_noise_scale=0.2, h_noise_scale=0.2
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) -> None:
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self.noise_scale = torch.Tensor(
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[x_noise_scale, y_noise_scale, w_noise_scale, h_noise_scale]
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)
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def __call__(self, refanchors: Tensor) -> Tensor:
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nq, bs, query_dim = refanchors.shape
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device = refanchors.device
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noise_raw = torch.rand_like(refanchors)
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noise_scale = self.noise_scale.to(device)[:query_dim]
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new_refanchors = refanchors * (1 + (noise_raw - 0.5) * noise_scale)
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return new_refanchors.clamp_(0, 1)
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def sigmoid_focal_loss(
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inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2, no_reduction=False
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):
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"""
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Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
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Args:
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inputs: A float tensor of arbitrary shape.
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The predictions for each example.
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targets: A float tensor with the same shape as inputs. Stores the binary
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classification label for each element in inputs
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(0 for the negative class and 1 for the positive class).
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alpha: (optional) Weighting factor in range (0,1) to balance
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positive vs negative examples. Default = -1 (no weighting).
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gamma: Exponent of the modulating factor (1 - p_t) to
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balance easy vs hard examples.
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Returns:
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Loss tensor
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"""
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prob = inputs.sigmoid()
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ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
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p_t = prob * targets + (1 - prob) * (1 - targets)
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loss = ce_loss * ((1 - p_t) ** gamma)
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if alpha >= 0:
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alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
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loss = alpha_t * loss
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if no_reduction:
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return loss
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return loss.mean(1).sum() / num_boxes
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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(
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nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
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)
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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 _get_activation_fn(activation, d_model=256, batch_dim=0):
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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 gen_sineembed_for_position(pos_tensor):
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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(128, dtype=torch.float32, device=pos_tensor.device)
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dim_t = 10000 ** (2 * (torch.div(dim_t, 2, rounding_mode='floor')) / 128)
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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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class ContrastiveEmbed(nn.Module):
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def __init__(self, max_text_len=256):
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"""
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Args:
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max_text_len: max length of text.
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"""
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super().__init__()
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self.max_text_len = max_text_len
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def forward(self, x, text_dict):
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"""_summary_
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Args:
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x (_type_): _description_
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text_dict (_type_): _description_
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{
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'encoded_text': encoded_text, # bs, 195, d_model
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'text_token_mask': text_token_mask, # bs, 195
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# True for used tokens. False for padding tokens
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}
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Returns:
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_type_: _description_
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"""
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assert isinstance(text_dict, dict)
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y = text_dict["encoded_text"]
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text_token_mask = text_dict["text_token_mask"]
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res = x @ y.transpose(-1, -2)
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res.masked_fill_(~text_token_mask[:, None, :], float("-inf"))
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# padding to max_text_len
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new_res = torch.full((*res.shape[:-1], self.max_text_len), float("-inf"), device=res.device)
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new_res[..., : res.shape[-1]] = res
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return new_res
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