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models.py
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from vision.backbone_utils import resnet_fpn_net
from constants import NUM_ROTATION
class PushNet(nn.Module):
"""
The DQN Network.
"""
def __init__(self, pre_train=False):
super().__init__()
self.device = torch.device("cuda")
self.pre_train = pre_train
self.num_rotations = NUM_ROTATION
# self.pushnet = FCN(2, 1).to(self.device)
self.pushnet = resnet_fpn_net(
"resnet34", trainable_layers=5, grasp=False, input_channels=2).to(self.device)
print("max_memory_allocated (MB):", torch.cuda.max_memory_allocated() / 2 ** 20)
print("memory_allocated (MB):", torch.cuda.memory_allocated() / 2 ** 20)
def forward(
self, input_data, is_volatile=False, specific_rotation=-1,
):
if self.pre_train:
output_probs = self.pushnet(input_data)
return output_probs
else:
if is_volatile:
with torch.no_grad():
output_prob = []
# 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()
.to(self.device)
)
flow_grid_before = F.affine_grid(
affine_mat_before, input_data.size(), align_corners=True
)
# Rotate images clockwise
rotate_data = F.grid_sample(
input_data.to(self.device),
flow_grid_before,
mode="bilinear",
align_corners=True,
)
final_push_feat = self.pushnet(rotate_data)
# 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()
.to(self.device)
)
flow_grid_after = F.affine_grid(
affine_mat_after, final_push_feat.data.size(), align_corners=True
)
# Forward pass through branches, undo rotation on output predictions, upsample results
output_prob.append(
F.grid_sample(
final_push_feat,
flow_grid_after,
mode="bilinear",
align_corners=True,
),
)
return output_prob
else:
raise NotImplementedError
# self.output_prob = []
# # Apply rotations to images
# 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().to(self.device)
# )
# affine_mat_before.requires_grad_(False)
# flow_grid_before = F.affine_grid(
# affine_mat_before, input_color_data.size(), align_corners=True
# )
# # Rotate images clockwise
# rotate_color = F.grid_sample(
# input_color_data.to(self.device),
# flow_grid_before,
# mode="bilinear",
# align_corners=True,
# )
# rotate_depth = F.grid_sample(
# input_depth_data.to(self.device),
# flow_grid_before,
# mode="bilinear",
# align_corners=True,
# )
# input_data = torch.cat((rotate_color, rotate_depth), dim=1)
# # Pass intermediate features to net
# final_push_feat = self.pushnet(input_data)
# # 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().to(self.device)
# )
# affine_mat_after.requires_grad_(False)
# flow_grid_after = F.affine_grid(
# affine_mat_after.to(self.device),
# final_push_feat.data.size(),
# align_corners=True,
# )
# # Forward pass through branches, undo rotation on output predictions, upsample results
# self.output_prob.append(
# F.grid_sample(
# final_push_feat, flow_grid_after, mode="bilinear", align_corners=True
# )
# )
# return self.output_prob
class reinforcement_net(nn.Module):
"""
The DQN Network.
graspnet is the Grasp Network.
pushnet is the Push Network for the DQN + GN method.
"""
def __init__(self, pre_train=False): # , snapshot=None
super(reinforcement_net, self).__init__()
self.device = torch.device("cuda")
self.pre_train = pre_train
self.num_rotations = NUM_ROTATION
if pre_train:
# self.pushnet = resnet_fpn_net(
# "resnet18", trainable_layers=5, grasp=False, input_channels=4
# ).to(self.device)
# self.pushnet = FCN(4, 1).to(self.device)
self.graspnet = resnet_fpn_net("resnet18", trainable_layers=5).to(self.device)
else:
# self.pushnet = resnet_fpn_net(
# "resnet18", trainable_layers=5, grasp=False, input_channels=4
# ).to(self.device)
# self.pushnet = FCN(4, 1).to(self.device)
self.graspnet = resnet_fpn_net("resnet18", trainable_layers=5).to(self.device)
print("max_memory_allocated (MB):", torch.cuda.max_memory_allocated() / 2 ** 20)
print("memory_allocated (MB):", torch.cuda.memory_allocated() / 2 ** 20)
def forward(
self,
input_color_data,
input_depth_data,
is_volatile=False,
specific_rotation=-1,
use_push=True,
push_only=False,
):
if self.pre_train:
input_data = torch.cat((input_color_data, input_depth_data), dim=1)
if use_push:
if push_only:
output_probs = self.pushnet(input_data)
else:
final_push_feat = self.pushnet(input_data)
final_grasp_feat = self.graspnet(input_data)
output_probs = (final_push_feat, final_grasp_feat)
else:
output_probs = self.graspnet(input_data)
return output_probs
else:
if is_volatile:
with torch.no_grad():
output_prob = []
# 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()
.to(self.device)
)
flow_grid_before = F.affine_grid(
affine_mat_before, input_color_data.size(), align_corners=True
)
# Rotate images clockwise
rotate_color = F.grid_sample(
input_color_data.to(self.device),
flow_grid_before,
mode="nearest",
align_corners=True,
)
rotate_depth = F.grid_sample(
input_depth_data.to(self.device),
flow_grid_before,
mode="nearest",
align_corners=True,
)
input_data = torch.cat((rotate_color, rotate_depth), dim=1)
# Pass intermediate features to net
if use_push:
final_push_feat = self.pushnet(input_data)
if not push_only:
final_grasp_feat = self.graspnet(input_data)
else:
final_grasp_feat = self.graspnet(input_data)
# 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()
.to(self.device)
)
if use_push:
flow_grid_after = F.affine_grid(
affine_mat_after, final_push_feat.data.size(), align_corners=True
)
else:
flow_grid_after = F.affine_grid(
affine_mat_after, final_grasp_feat.data.size(), align_corners=True
)
# Forward pass through branches, undo rotation on output predictions, upsample results
if use_push:
if push_only:
output_prob.append(
F.grid_sample(
final_push_feat,
flow_grid_after,
mode="nearest",
align_corners=True,
),
)
else:
output_prob.append(
[
F.grid_sample(
final_push_feat,
flow_grid_after,
mode="nearest",
align_corners=True,
),
F.grid_sample(
final_grasp_feat,
flow_grid_after,
mode="nearest",
align_corners=True,
),
]
)
else:
output_prob.append(
[
None,
F.grid_sample(
final_grasp_feat,
flow_grid_after,
mode="nearest",
align_corners=True,
),
]
)
return output_prob
else:
self.output_prob = []
# Apply rotations to images
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().to(self.device)
)
affine_mat_before.requires_grad_(False)
flow_grid_before = F.affine_grid(
affine_mat_before, input_color_data.size(), align_corners=True
)
# Rotate images clockwise
rotate_color = F.grid_sample(
input_color_data.to(self.device),
flow_grid_before,
mode="nearest",
align_corners=True,
)
rotate_depth = F.grid_sample(
input_depth_data.to(self.device),
flow_grid_before,
mode="nearest",
align_corners=True,
)
input_data = torch.cat((rotate_color, rotate_depth), dim=1)
# Pass intermediate features to net
final_push_feat = self.pushnet(input_data)
if not push_only:
final_grasp_feat = self.graspnet(input_data)
# 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().to(self.device)
)
affine_mat_after.requires_grad_(False)
flow_grid_after = F.affine_grid(
affine_mat_after.to(self.device),
final_push_feat.data.size(),
align_corners=True,
)
# Forward pass through branches, undo rotation on output predictions, upsample results
if push_only:
self.output_prob.append(
F.grid_sample(
final_push_feat, flow_grid_after, mode="nearest", align_corners=True
)
)
else:
self.output_prob.append(
[
F.grid_sample(
final_push_feat, flow_grid_after, mode="nearest", align_corners=True
),
F.grid_sample(
final_grasp_feat,
flow_grid_after,
mode="nearest",
align_corners=True,
),
]
)
return self.output_prob