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"""Attention module."""
import numpy as np
import torch
import torch.nn.functional as F
from cliport.models.core.attention import Attention
class AttentionImageGoal(Attention):
"""Attention (a.k.a Pick) with image-goals module."""
def __init__(self, stream_fcn, in_shape, n_rotations, preprocess, cfg, device):
super().__init__(stream_fcn, in_shape, n_rotations, preprocess, cfg, device)
def forward(self, inp_img, goal_img, softmax=True):
"""Forward pass."""
# Input image.
in_data = np.pad(inp_img, self.padding, mode='constant')
in_shape = (1,) + in_data.shape
in_data = in_data.reshape(in_shape)
in_tens = torch.from_numpy(in_data).to(dtype=torch.float, device=self.device)
goal_tensor = np.pad(goal_img, self.padding, mode='constant')
goal_shape = (1,) + goal_tensor.shape
goal_tensor = goal_tensor.reshape(goal_shape)
goal_tensor = torch.from_numpy(goal_tensor.copy()).to(dtype=torch.float, device=self.device)
in_tens = in_tens * goal_tensor
# Rotation pivot.
pv = np.array(in_data.shape[1:3]) // 2
# Rotate input.
in_tens = in_tens.permute(0, 3, 1, 2)
in_tens = in_tens.repeat(self.n_rotations, 1, 1, 1)
in_tens = self.rotator(in_tens, pivot=pv)
# Forward pass.
logits = []
for x in in_tens:
logits.append(self.attend(x))
logits = torch.cat(logits, dim=0)
# Rotate back output.
logits = self.rotator(logits, reverse=True, pivot=pv)
logits = torch.cat(logits, dim=0)
c0 = self.padding[:2, 0]
c1 = c0 + inp_img.shape[:2]
logits = logits[:, :, c0[0]:c1[0], c0[1]:c1[1]]
logits = logits.permute(1, 2, 3, 0) # D H W C
output = logits.reshape(1, np.prod(logits.shape))
if softmax:
output = F.softmax(output, dim=-1)
output = output.reshape(logits.shape[1:])
return output |