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models.py
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models.py
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#!/usr/bin/env python
from collections import OrderedDict
import numpy as np
from scipy import ndimage
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torchvision
import matplotlib.pyplot as plt
import time
class reactive_net(nn.Module):
def __init__(self, use_cuda): # , snapshot=None
super(reactive_net, self).__init__()
self.use_cuda = use_cuda
# Initialize network trunks with DenseNet pre-trained on ImageNet
self.push_color_trunk = torchvision.models.densenet.densenet121(pretrained=True)
self.push_depth_trunk = torchvision.models.densenet.densenet121(pretrained=True)
self.grasp_color_trunk = torchvision.models.densenet.densenet121(pretrained=True)
self.grasp_depth_trunk = torchvision.models.densenet.densenet121(pretrained=True)
self.num_rotations = 16
# Construct network branches for pushing and grasping
self.pushnet = nn.Sequential(OrderedDict([
('push-norm0', nn.BatchNorm2d(2048)),
('push-relu0', nn.ReLU(inplace=True)),
('push-conv0', nn.Conv2d(2048, 64, kernel_size=1, stride=1, bias=False)),
('push-norm1', nn.BatchNorm2d(64)),
('push-relu1', nn.ReLU(inplace=True)),
('push-conv1', nn.Conv2d(64, 3, kernel_size=1, stride=1, bias=False))
# ('push-upsample2', nn.Upsample(scale_factor=4, mode='bilinear'))
]))
self.graspnet = nn.Sequential(OrderedDict([
('grasp-norm0', nn.BatchNorm2d(2048)),
('grasp-relu0', nn.ReLU(inplace=True)),
('grasp-conv0', nn.Conv2d(2048, 64, kernel_size=1, stride=1, bias=False)),
('grasp-norm1', nn.BatchNorm2d(64)),
('grasp-relu1', nn.ReLU(inplace=True)),
('grasp-conv1', nn.Conv2d(64, 3, kernel_size=1, stride=1, bias=False))
# ('grasp-upsample2', nn.Upsample(scale_factor=4, mode='bilinear'))
]))
# Initialize network weights
for m in self.named_modules():
if 'push-' in m[0] or 'grasp-' in m[0]:
if isinstance(m[1], nn.Conv2d):
nn.init.kaiming_normal(m[1].weight.data)
elif isinstance(m[1], nn.BatchNorm2d):
m[1].weight.data.fill_(1)
m[1].bias.data.zero_()
# Initialize output variable (for backprop)
self.interm_feat = []
self.output_prob = []
def forward(self, input_color_data, input_depth_data, is_volatile=False, specific_rotation=-1):
if is_volatile:
output_prob = []
interm_feat = []
# Apply rotations to images
for rotate_idx in range(self.num_rotations):
rotate_theta = np.radians(rotate_idx*(360/self.num_rotations))
# Compute sample grid for rotation BEFORE neural network
affine_mat_before = np.asarray([[np.cos(-rotate_theta), np.sin(-rotate_theta), 0],[-np.sin(-rotate_theta), np.cos(-rotate_theta), 0]])
affine_mat_before.shape = (2,3,1)
affine_mat_before = torch.from_numpy(affine_mat_before).permute(2,0,1).float()
if self.use_cuda:
flow_grid_before = F.affine_grid(Variable(affine_mat_before, requires_grad=False).cuda(), input_color_data.size())
else:
flow_grid_before = F.affine_grid(Variable(affine_mat_before, requires_grad=False), input_color_data.size())
# Rotate images clockwise
if self.use_cuda:
rotate_color = F.grid_sample(Variable(input_color_data, volatile=True).cuda(), flow_grid_before, mode='nearest')
rotate_depth = F.grid_sample(Variable(input_depth_data, volatile=True).cuda(), flow_grid_before, mode='nearest')
else:
rotate_color = F.grid_sample(Variable(input_color_data, volatile=True), flow_grid_before, mode='nearest')
rotate_depth = F.grid_sample(Variable(input_depth_data, volatile=True), flow_grid_before, mode='nearest')
# Compute intermediate features
interm_push_color_feat = self.push_color_trunk.features(rotate_color)
interm_push_depth_feat = self.push_depth_trunk.features(rotate_depth)
interm_push_feat = torch.cat((interm_push_color_feat, interm_push_depth_feat), dim=1)
interm_grasp_color_feat = self.grasp_color_trunk.features(rotate_color)
interm_grasp_depth_feat = self.grasp_depth_trunk.features(rotate_depth)
interm_grasp_feat = torch.cat((interm_grasp_color_feat, interm_grasp_depth_feat), dim=1)
interm_feat.append([interm_push_feat, interm_grasp_feat])
# Compute sample grid for rotation AFTER branches
affine_mat_after = np.asarray([[np.cos(rotate_theta), np.sin(rotate_theta), 0],[-np.sin(rotate_theta), np.cos(rotate_theta), 0]])
affine_mat_after.shape = (2,3,1)
affine_mat_after = torch.from_numpy(affine_mat_after).permute(2,0,1).float()
if self.use_cuda:
flow_grid_after = F.affine_grid(Variable(affine_mat_after, requires_grad=False).cuda(), interm_push_feat.data.size())
else:
flow_grid_after = F.affine_grid(Variable(affine_mat_after, requires_grad=False), interm_push_feat.data.size())
# Forward pass through branches, undo rotation on output predictions, upsample results
output_prob.append([nn.Upsample(scale_factor=16, mode='bilinear').forward(F.grid_sample(self.pushnet(interm_push_feat), flow_grid_after, mode='nearest')),
nn.Upsample(scale_factor=16, mode='bilinear').forward(F.grid_sample(self.graspnet(interm_grasp_feat), flow_grid_after, mode='nearest'))])
return output_prob, interm_feat
else:
self.output_prob = []
self.interm_feat = []
# Apply rotations to intermediate features
# for rotate_idx in range(self.num_rotations):
rotate_idx = specific_rotation
rotate_theta = np.radians(rotate_idx*(360/self.num_rotations))
# Compute sample grid for rotation BEFORE branches
affine_mat_before = np.asarray([[np.cos(-rotate_theta), np.sin(-rotate_theta), 0],[-np.sin(-rotate_theta), np.cos(-rotate_theta), 0]])
affine_mat_before.shape = (2,3,1)
affine_mat_before = torch.from_numpy(affine_mat_before).permute(2,0,1).float()
if self.use_cuda:
flow_grid_before = F.affine_grid(Variable(affine_mat_before, requires_grad=False).cuda(), input_color_data.size())
else:
flow_grid_before = F.affine_grid(Variable(affine_mat_before, requires_grad=False), input_color_data.size())
# Rotate images clockwise
if self.use_cuda:
rotate_color = F.grid_sample(Variable(input_color_data, requires_grad=False).cuda(), flow_grid_before, mode='nearest')
rotate_depth = F.grid_sample(Variable(input_depth_data, requires_grad=False).cuda(), flow_grid_before, mode='nearest')
else:
rotate_color = F.grid_sample(Variable(input_color_data, requires_grad=False), flow_grid_before, mode='nearest')
rotate_depth = F.grid_sample(Variable(input_depth_data, requires_grad=False), flow_grid_before, mode='nearest')
# Compute intermediate features
interm_push_color_feat = self.push_color_trunk.features(rotate_color)
interm_push_depth_feat = self.push_depth_trunk.features(rotate_depth)
interm_push_feat = torch.cat((interm_push_color_feat, interm_push_depth_feat), dim=1)
interm_grasp_color_feat = self.grasp_color_trunk.features(rotate_color)
interm_grasp_depth_feat = self.grasp_depth_trunk.features(rotate_depth)
interm_grasp_feat = torch.cat((interm_grasp_color_feat, interm_grasp_depth_feat), dim=1)
self.interm_feat.append([interm_push_feat, interm_grasp_feat])
# Compute sample grid for rotation AFTER branches
affine_mat_after = np.asarray([[np.cos(rotate_theta), np.sin(rotate_theta), 0],[-np.sin(rotate_theta), np.cos(rotate_theta), 0]])
affine_mat_after.shape = (2,3,1)
affine_mat_after = torch.from_numpy(affine_mat_after).permute(2,0,1).float()
if self.use_cuda:
flow_grid_after = F.affine_grid(Variable(affine_mat_after, requires_grad=False).cuda(), interm_push_feat.data.size())
else:
flow_grid_after = F.affine_grid(Variable(affine_mat_after, requires_grad=False), interm_push_feat.data.size())
# Forward pass through branches, undo rotation on output predictions, upsample results
self.output_prob.append([nn.Upsample(scale_factor=16, mode='bilinear').forward(F.grid_sample(self.pushnet(interm_push_feat), flow_grid_after, mode='nearest')),
nn.Upsample(scale_factor=16, mode='bilinear').forward(F.grid_sample(self.graspnet(interm_grasp_feat), flow_grid_after, mode='nearest'))])
return self.output_prob, self.interm_feat
class reinforcement_net(nn.Module):
def __init__(self, use_cuda): # , snapshot=None
super(reinforcement_net, self).__init__()
self.use_cuda = use_cuda
# Initialize network trunks with DenseNet pre-trained on ImageNet
self.push_color_trunk = torchvision.models.densenet.densenet121(pretrained=True)
self.push_depth_trunk = torchvision.models.densenet.densenet121(pretrained=True)
self.grasp_color_trunk = torchvision.models.densenet.densenet121(pretrained=True)
self.grasp_depth_trunk = torchvision.models.densenet.densenet121(pretrained=True)
self.num_rotations = 16
# Construct network branches for pushing and grasping
self.pushnet = nn.Sequential(OrderedDict([
('push-norm0', nn.BatchNorm2d(2048)),
('push-relu0', nn.ReLU(inplace=True)),
('push-conv0', nn.Conv2d(2048, 64, kernel_size=1, stride=1, bias=False)),
('push-norm1', nn.BatchNorm2d(64)),
('push-relu1', nn.ReLU(inplace=True)),
('push-conv1', nn.Conv2d(64, 1, kernel_size=1, stride=1, bias=False))
# ('push-upsample2', nn.Upsample(scale_factor=4, mode='bilinear'))
]))
self.graspnet = nn.Sequential(OrderedDict([
('grasp-norm0', nn.BatchNorm2d(2048)),
('grasp-relu0', nn.ReLU(inplace=True)),
('grasp-conv0', nn.Conv2d(2048, 64, kernel_size=1, stride=1, bias=False)),
('grasp-norm1', nn.BatchNorm2d(64)),
('grasp-relu1', nn.ReLU(inplace=True)),
('grasp-conv1', nn.Conv2d(64, 1, kernel_size=1, stride=1, bias=False))
# ('grasp-upsample2', nn.Upsample(scale_factor=4, mode='bilinear'))
]))
# Initialize network weights
for m in self.named_modules():
if 'push-' in m[0] or 'grasp-' in m[0]:
if isinstance(m[1], nn.Conv2d):
nn.init.kaiming_normal(m[1].weight.data)
elif isinstance(m[1], nn.BatchNorm2d):
m[1].weight.data.fill_(1)
m[1].bias.data.zero_()
# Initialize output variable (for backprop)
self.interm_feat = []
self.output_prob = []
def forward(self, input_color_data, input_depth_data, is_volatile=False, specific_rotation=-1):
if is_volatile:
with torch.no_grad():
output_prob = []
interm_feat = []
# Apply rotations to images
for rotate_idx in range(self.num_rotations):
rotate_theta = np.radians(rotate_idx*(360/self.num_rotations))
# Compute sample grid for rotation BEFORE neural network
affine_mat_before = np.asarray([[np.cos(-rotate_theta), np.sin(-rotate_theta), 0],[-np.sin(-rotate_theta), np.cos(-rotate_theta), 0]])
affine_mat_before.shape = (2,3,1)
affine_mat_before = torch.from_numpy(affine_mat_before).permute(2,0,1).float()
if self.use_cuda:
flow_grid_before = F.affine_grid(Variable(affine_mat_before, requires_grad=False).cuda(), input_color_data.size())
else:
flow_grid_before = F.affine_grid(Variable(affine_mat_before, requires_grad=False), input_color_data.size())
# Rotate images clockwise
if self.use_cuda:
rotate_color = F.grid_sample(Variable(input_color_data, volatile=True).cuda(), flow_grid_before, mode='nearest')
rotate_depth = F.grid_sample(Variable(input_depth_data, volatile=True).cuda(), flow_grid_before, mode='nearest')
else:
rotate_color = F.grid_sample(Variable(input_color_data, volatile=True), flow_grid_before, mode='nearest')
rotate_depth = F.grid_sample(Variable(input_depth_data, volatile=True), flow_grid_before, mode='nearest')
# Compute intermediate features
interm_push_color_feat = self.push_color_trunk.features(rotate_color)
interm_push_depth_feat = self.push_depth_trunk.features(rotate_depth)
interm_push_feat = torch.cat((interm_push_color_feat, interm_push_depth_feat), dim=1)
interm_grasp_color_feat = self.grasp_color_trunk.features(rotate_color)
interm_grasp_depth_feat = self.grasp_depth_trunk.features(rotate_depth)
interm_grasp_feat = torch.cat((interm_grasp_color_feat, interm_grasp_depth_feat), dim=1)
interm_feat.append([interm_push_feat, interm_grasp_feat])
# Compute sample grid for rotation AFTER branches
affine_mat_after = np.asarray([[np.cos(rotate_theta), np.sin(rotate_theta), 0],[-np.sin(rotate_theta), np.cos(rotate_theta), 0]])
affine_mat_after.shape = (2,3,1)
affine_mat_after = torch.from_numpy(affine_mat_after).permute(2,0,1).float()
if self.use_cuda:
flow_grid_after = F.affine_grid(Variable(affine_mat_after, requires_grad=False).cuda(), interm_push_feat.data.size())
else:
flow_grid_after = F.affine_grid(Variable(affine_mat_after, requires_grad=False), interm_push_feat.data.size())
# Forward pass through branches, undo rotation on output predictions, upsample results
output_prob.append([nn.Upsample(scale_factor=16, mode='bilinear').forward(F.grid_sample(self.pushnet(interm_push_feat), flow_grid_after, mode='nearest')),
nn.Upsample(scale_factor=16, mode='bilinear').forward(F.grid_sample(self.graspnet(interm_grasp_feat), flow_grid_after, mode='nearest'))])
return output_prob, interm_feat
else:
self.output_prob = []
self.interm_feat = []
# Apply rotations to intermediate features
# for rotate_idx in range(self.num_rotations):
rotate_idx = specific_rotation
rotate_theta = np.radians(rotate_idx*(360/self.num_rotations))
# Compute sample grid for rotation BEFORE branches
affine_mat_before = np.asarray([[np.cos(-rotate_theta), np.sin(-rotate_theta), 0],[-np.sin(-rotate_theta), np.cos(-rotate_theta), 0]])
affine_mat_before.shape = (2,3,1)
affine_mat_before = torch.from_numpy(affine_mat_before).permute(2,0,1).float()
if self.use_cuda:
flow_grid_before = F.affine_grid(Variable(affine_mat_before, requires_grad=False).cuda(), input_color_data.size())
else:
flow_grid_before = F.affine_grid(Variable(affine_mat_before, requires_grad=False), input_color_data.size())
# Rotate images clockwise
if self.use_cuda:
rotate_color = F.grid_sample(Variable(input_color_data, requires_grad=False).cuda(), flow_grid_before, mode='nearest')
rotate_depth = F.grid_sample(Variable(input_depth_data, requires_grad=False).cuda(), flow_grid_before, mode='nearest')
else:
rotate_color = F.grid_sample(Variable(input_color_data, requires_grad=False), flow_grid_before, mode='nearest')
rotate_depth = F.grid_sample(Variable(input_depth_data, requires_grad=False), flow_grid_before, mode='nearest')
# Compute intermediate features
interm_push_color_feat = self.push_color_trunk.features(rotate_color)
interm_push_depth_feat = self.push_depth_trunk.features(rotate_depth)
interm_push_feat = torch.cat((interm_push_color_feat, interm_push_depth_feat), dim=1)
interm_grasp_color_feat = self.grasp_color_trunk.features(rotate_color)
interm_grasp_depth_feat = self.grasp_depth_trunk.features(rotate_depth)
interm_grasp_feat = torch.cat((interm_grasp_color_feat, interm_grasp_depth_feat), dim=1)
self.interm_feat.append([interm_push_feat, interm_grasp_feat])
# Compute sample grid for rotation AFTER branches
affine_mat_after = np.asarray([[np.cos(rotate_theta), np.sin(rotate_theta), 0],[-np.sin(rotate_theta), np.cos(rotate_theta), 0]])
affine_mat_after.shape = (2,3,1)
affine_mat_after = torch.from_numpy(affine_mat_after).permute(2,0,1).float()
if self.use_cuda:
flow_grid_after = F.affine_grid(Variable(affine_mat_after, requires_grad=False).cuda(), interm_push_feat.data.size())
else:
flow_grid_after = F.affine_grid(Variable(affine_mat_after, requires_grad=False), interm_push_feat.data.size())
# Forward pass through branches, undo rotation on output predictions, upsample results
self.output_prob.append([nn.Upsample(scale_factor=16, mode='bilinear').forward(F.grid_sample(self.pushnet(interm_push_feat), flow_grid_after, mode='nearest')),
nn.Upsample(scale_factor=16, mode='bilinear').forward(F.grid_sample(self.graspnet(interm_grasp_feat), flow_grid_after, mode='nearest'))])
return self.output_prob, self.interm_feat
# # OLD VERSION: IMPLICIT ROTATION INSIDE
# def forward(self, input_color_data, input_depth_data, is_volatile=False):
# # Run forward pass through trunk to get intermediate features
# if is_volatile:
# interm_color_feat = self.color_trunk.features(Variable(input_color_data, volatile=True).cuda())
# interm_depth_feat = self.depth_trunk.features(Variable(input_depth_data, volatile=True).cuda())
# interm_feat = torch.cat((interm_color_feat, interm_depth_feat), dim=1)
# output_prob = []
# # Apply rotations to intermediate features
# for rotate_idx in range(self.num_rotations):
# rotate_theta = np.radians(rotate_idx*(360/self.num_rotations))
# # Compute sample grid for rotation BEFORE branches
# affine_mat_before = np.asarray([[np.cos(-rotate_theta), np.sin(-rotate_theta), 0],[-np.sin(-rotate_theta), np.cos(-rotate_theta), 0]])
# affine_mat_before.shape = (2,3,1)
# affine_mat_before = torch.from_numpy(affine_mat_before).permute(2,0,1).float()
# flow_grid_before = F.affine_grid(Variable(affine_mat_before, requires_grad=False).cuda(), interm_feat.data.size())
# # Rotate intermediate features clockwise
# rotate_feat = F.grid_sample(interm_feat, flow_grid_before, mode='nearest')
# # test = rotate_feat.cpu().data.numpy()
# # test = np.sum(test[0,:,:,:], axis=0)
# # plt.imshow(test)
# # plt.show()
# # Compute sample grid for rotation AFTER branches
# affine_mat_after = np.asarray([[np.cos(rotate_theta), np.sin(rotate_theta), 0],[-np.sin(rotate_theta), np.cos(rotate_theta), 0]])
# affine_mat_after.shape = (2,3,1)
# affine_mat_after = torch.from_numpy(affine_mat_after).permute(2,0,1).float()
# flow_grid_after = F.affine_grid(Variable(affine_mat_after, requires_grad=False).cuda(), rotate_feat.data.size())
# # Forward pass through branches, undo rotation on output predictions, upsample results
# output_prob.append([nn.Upsample(scale_factor=16, mode='bilinear').forward(F.grid_sample(self.pushnet(rotate_feat), flow_grid_after, mode='nearest')),
# nn.Upsample(scale_factor=16, mode='bilinear').forward(F.grid_sample(self.graspnet(rotate_feat), flow_grid_after, mode='nearest'))])
# return output_prob, interm_feat
# else:
# interm_color_feat = self.color_trunk.features(Variable(input_color_data, requires_grad=False).cuda())
# interm_depth_feat = self.depth_trunk.features(Variable(input_depth_data, requires_grad=False).cuda())
# self.interm_feat = torch.cat((interm_color_feat, interm_depth_feat), dim=1)
# self.output_prob = []
# # Apply rotations to intermediate features
# # for rotate_idx in range(self.num_rotations):
# rotate_idx = specific_rotation
# rotate_theta = np.radians(rotate_idx*(360/self.num_rotations))
# # Compute sample grid for rotation BEFORE branches
# affine_mat_before = np.asarray([[np.cos(-rotate_theta), np.sin(-rotate_theta), 0],[-np.sin(-rotate_theta), np.cos(-rotate_theta), 0]])
# affine_mat_before.shape = (2,3,1)
# affine_mat_before = torch.from_numpy(affine_mat_before).permute(2,0,1).float()
# flow_grid_before = F.affine_grid(Variable(affine_mat_before, requires_grad=False).cuda(), self.interm_feat.data.size())
# # Rotate intermediate features clockwise
# rotate_feat = F.grid_sample(self.interm_feat, flow_grid_before, mode='nearest')
# # Compute sample grid for rotation AFTER branches
# affine_mat_after = np.asarray([[np.cos(rotate_theta), np.sin(rotate_theta), 0],[-np.sin(rotate_theta), np.cos(rotate_theta), 0]])
# affine_mat_after.shape = (2,3,1)
# affine_mat_after = torch.from_numpy(affine_mat_after).permute(2,0,1).float()
# flow_grid_after = F.affine_grid(Variable(affine_mat_after, requires_grad=False).cuda(), rotate_feat.data.size())
# # Forward pass through branches, undo rotation on output predictions, upsample results
# self.output_prob.append([nn.Upsample(scale_factor=16, mode='bilinear').forward(F.grid_sample(self.pushnet(rotate_feat), flow_grid_after, mode='nearest')),
# nn.Upsample(scale_factor=16, mode='bilinear').forward(F.grid_sample(self.graspnet(rotate_feat), flow_grid_after, mode='nearest'))])
# return self.output_prob, self.interm_feat