1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174 | import os
import sys
import numpy as np
import torch
import torch.nn.functional as F
import torchvision.models as models
from stack import train as stack_train
class StackModel:
def __init__(self, root, loss, lam, output_stack, detach=False):
assert output_stack in [1, 2, 3, 4]
self._output_stack = output_stack
print(f"{loss}_{lam}_{detach}_False loading....")
self._model = stack_train.Trainer.load_model(f"{root}/dependencies/stack", f"{loss}_{lam}_{detach}_False")
self._model.eval()
def __call__(self, x):
# x: b x n x t x h x w
x = x.cuda()
out = self._model(x.unsqueeze(0), layer=self._output_stack)
out = out[0].permute(1, 0, 2, 3).flatten(1, 3)
return out.cpu()
class PrednetModel:
INPUT_LEN = 10 # Seems like this is the cap enforced by the prednet model
def __init__(self, root, output_stack):
assert output_stack in ("E0", "E1", "E2", "E3")
# Load model specific dependencies
sys.path.append(os.path.join(root, "dependencies", "prednet"))
from keras.models import model_from_json
from prednet import PredNet
self.output_stack = output_stack # starts at 'E0'
# Load the prednet model
weights_dir = os.path.join(root, 'dependencies', 'prednet', 'model_data_keras2')
weights_file = os.path.join(weights_dir, 'tensorflow_weights/prednet_kitti_weights.hdf5')
json_file = os.path.join(weights_dir, 'prednet_kitti_model.json')
# Load trained model
f = open(json_file, 'r')
json_string = f.read()
f.close()
prednet_config = model_from_json(json_string, custom_objects={'PredNet': PredNet})
prednet_config.load_weights(weights_file)
# Set the output to the provided layer
layer_config = prednet_config.layers[1].get_config()
layer_config['output_mode'] = output_stack
self.prednet = PredNet(weights=prednet_config.layers[1].get_weights(), **layer_config)
self.model = None
def __call__(self, x):
import keras
from keras.layers import Input
# x: chan x time x height x width
if self.model is None:
channels = 3
h, w = x.shape[2], x.shape[3]
if h == 73 and w == 73: # Little fix to ensure we can base in data scaled at 0.66x
h, w = 80, 80
input_shape = [PrednetModel.INPUT_LEN, channels, h, w]
print(input_shape)
inputs = Input(shape=tuple(input_shape))
predictions = self.prednet(inputs)
self.model = keras.models.Model(inputs=inputs, outputs=predictions)
h, w = x.shape[2], x.shape[3]
if h == 73 and w == 73: # Little fix to ensure we can base in data scaled at 0.66x
x = F.pad(x, (3, 4, 3, 4))
assert x.shape[2] % 2**3 == 0 # A requirement of the prednet model
x = x.repeat(3, 1, 1, 1) # Repeat the grayscale channel three times
x = x.permute(1, 0, 2, 3) # b x c x t x h x w -> b x t x c x h x w
x = x.unsqueeze(0) # Add batch dimension
x = x[:, -PrednetModel.INPUT_LEN:, ] # Only look at INPUT_LEN last frames
x = self.model.predict(x.numpy(), PrednetModel.INPUT_LEN) # Get hidden activity
x = torch.from_numpy(x[0]).flatten(start_dim=1, end_dim=-1) # time x neurons
return x
class ImgModelBase:
def __init__(self, n_warmup=None):
self.n_warmup = n_warmup
def __call__(self, x):
# x: chan x time x height x width
assert x.shape[0] == 1
x = x[0]
if self.n_warmup is not None:
x = x[self.n_warmup:]
activity_list = []
for t in range(x.shape[0]):
activity_list.append(self.model_output(x[t]))
activity = torch.stack(activity_list) # time x neurons
# if self.n_warmup is not None:
# return F.pad(activity, (0, 0, self.n_warmup, 0))
# else:
return activity.detach().cpu()
def model_output(self, x):
raise NotImplementedError
class BWTModel(ImgModelBase):
def __init__(self, root, n_warmup=None):
super().__init__(n_warmup)
# Load model specific dependencies
from oct2py import octave
octave.addpath(os.path.join(root, "dependencies", "bwt"))
self.bwt = octave.bwt_v1_octave
def model_output(self, x):
h, w = x.shape
target_dim = 3 ** int(np.ceil(np.log(h)/np.log(3)))
h_pad = target_dim - h
w_pad = target_dim - w
xt = F.pad(x, (0, w_pad, 0, h_pad))
return torch.from_numpy(self.bwt(xt.numpy())).flatten()
class VGGModel(ImgModelBase):
def __init__(self, layer, n_warmup=None):
super().__init__(n_warmup)
self.vgg = models.vgg16(pretrained=True).cuda()
self.layer = layer
def model_output(self, x):
x = x.cuda()
if self.layer == '2.1':
self.layer = 6
elif self.layer == '2.2':
self.layer = 8
elif self.layer == '3.1':
self.layer = 11
elif self.layer == '3.2':
self.layer = 13
elif self.layer == '3.3':
self.layer = 15
x = x.unsqueeze(0).unsqueeze(0) # Add back batch and channel dim
x = x.repeat(1, 3, 1, 1) # Repeat the grayscale image three times for the rgb channels
with torch.no_grad():
for i in range(self.layer + 1):
x = self.vgg.features[i](x)
return x.flatten()
|