-
Notifications
You must be signed in to change notification settings - Fork 183
/
predict_augmented_npy_8433n_maxout2048_extradense_pysex.py
242 lines (163 loc) · 9.5 KB
/
predict_augmented_npy_8433n_maxout2048_extradense_pysex.py
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
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
"""
Load an analysis file and redo the predictions on the validation set / test set,
this time with augmented data and averaging. Store them as numpy files.
"""
import numpy as np
# import pandas as pd
import theano
import theano.tensor as T
import layers
import cc_layers
import custom
import load_data
import realtime_augmentation as ra
import time
import csv
import os
import cPickle as pickle
BATCH_SIZE = 32 # 16
NUM_INPUT_FEATURES = 3
CHUNK_SIZE = 8000 # 10000 # this should be a multiple of the batch size
# ANALYSIS_PATH = "analysis/try_convnet_cc_multirot_3x69r45_untied_bias.pkl"
ANALYSIS_PATH = "analysis/final/try_convnet_cc_multirotflip_3x69r45_8433n_maxout2048_extradense_pysex.pkl"
DO_VALID = True # disable this to not bother with the validation set evaluation
DO_TEST = True # disable this to not generate predictions on the testset
target_filename = os.path.basename(ANALYSIS_PATH).replace(".pkl", ".npy.gz")
target_path_valid = os.path.join("predictions/final/augmented/valid", target_filename)
target_path_test = os.path.join("predictions/final/augmented/test", target_filename)
print "Loading model data etc."
analysis = np.load(ANALYSIS_PATH)
input_sizes = [(69, 69), (69, 69)]
ds_transforms = [
ra.build_ds_transform(3.0, target_size=input_sizes[0]),
ra.build_ds_transform(3.0, target_size=input_sizes[1]) + ra.build_augmentation_transform(rotation=45)]
num_input_representations = len(ds_transforms)
# split training data into training + a small validation set
num_train = load_data.num_train
num_valid = num_train // 10 # integer division
num_train -= num_valid
num_test = load_data.num_test
valid_ids = load_data.train_ids[num_train:]
train_ids = load_data.train_ids[:num_train]
test_ids = load_data.test_ids
train_indices = np.arange(num_train)
valid_indices = np.arange(num_train, num_train+num_valid)
test_indices = np.arange(num_test)
y_valid = np.load("data/solutions_train.npy")[num_train:]
print "Build model"
l0 = layers.Input2DLayer(BATCH_SIZE, NUM_INPUT_FEATURES, input_sizes[0][0], input_sizes[0][1])
l0_45 = layers.Input2DLayer(BATCH_SIZE, NUM_INPUT_FEATURES, input_sizes[1][0], input_sizes[1][1])
l0r = layers.MultiRotSliceLayer([l0, l0_45], part_size=45, include_flip=True)
l0s = cc_layers.ShuffleBC01ToC01BLayer(l0r)
l1a = cc_layers.CudaConvnetConv2DLayer(l0s, n_filters=32, filter_size=8, weights_std=0.01, init_bias_value=0.1, dropout=0.0, partial_sum=1, untie_biases=True)
l1 = cc_layers.CudaConvnetPooling2DLayer(l1a, pool_size=2)
l2a = cc_layers.CudaConvnetConv2DLayer(l1, n_filters=64, filter_size=4, weights_std=0.01, init_bias_value=0.1, dropout=0.0, partial_sum=1, untie_biases=True)
l2 = cc_layers.CudaConvnetPooling2DLayer(l2a, pool_size=2)
l3a = cc_layers.CudaConvnetConv2DLayer(l2, n_filters=128, filter_size=3, weights_std=0.01, init_bias_value=0.1, dropout=0.0, partial_sum=1, untie_biases=True)
l3b = cc_layers.CudaConvnetConv2DLayer(l3a, n_filters=128, filter_size=3, pad=0, weights_std=0.1, init_bias_value=0.1, dropout=0.0, partial_sum=1, untie_biases=True)
l3 = cc_layers.CudaConvnetPooling2DLayer(l3b, pool_size=2)
l3s = cc_layers.ShuffleC01BToBC01Layer(l3)
j3 = layers.MultiRotMergeLayer(l3s, num_views=4) # 2) # merge convolutional parts
l4a = layers.DenseLayer(j3, n_outputs=4096, weights_std=0.001, init_bias_value=0.01, dropout=0.5, nonlinearity=layers.identity)
l4b = layers.FeatureMaxPoolingLayer(l4a, pool_size=2, feature_dim=1, implementation='reshape')
l4c = layers.DenseLayer(l4b, n_outputs=4096, weights_std=0.001, init_bias_value=0.01, dropout=0.5, nonlinearity=layers.identity)
l4 = layers.FeatureMaxPoolingLayer(l4c, pool_size=2, feature_dim=1, implementation='reshape')
# l5 = layers.DenseLayer(l4, n_outputs=37, weights_std=0.01, init_bias_value=0.0, dropout=0.5, nonlinearity=custom.clip_01) # nonlinearity=layers.identity)
l5 = layers.DenseLayer(l4, n_outputs=37, weights_std=0.01, init_bias_value=0.1, dropout=0.5, nonlinearity=layers.identity)
# l6 = layers.OutputLayer(l5, error_measure='mse')
l6 = custom.OptimisedDivGalaxyOutputLayer(l5) # this incorporates the constraints on the output (probabilities sum to one, weighting, etc.)
xs_shared = [theano.shared(np.zeros((1,1,1,1), dtype=theano.config.floatX)) for _ in xrange(num_input_representations)]
idx = T.lscalar('idx')
givens = {
l0.input_var: xs_shared[0][idx*BATCH_SIZE:(idx+1)*BATCH_SIZE],
l0_45.input_var: xs_shared[1][idx*BATCH_SIZE:(idx+1)*BATCH_SIZE],
}
compute_output = theano.function([idx], l6.predictions(dropout_active=False), givens=givens)
print "Load model parameters"
layers.set_param_values(l6, analysis['param_values'])
print "Create generators"
# set here which transforms to use to make predictions
augmentation_transforms = []
for zoom in [1 / 1.2, 1.0, 1.2]:
for angle in np.linspace(0, 360, 10, endpoint=False):
augmentation_transforms.append(ra.build_augmentation_transform(rotation=angle, zoom=zoom))
augmentation_transforms.append(ra.build_augmentation_transform(rotation=(angle + 180), zoom=zoom, shear=180)) # flipped
print " %d augmentation transforms." % len(augmentation_transforms)
augmented_data_gen_valid = ra.realtime_fixed_augmented_data_gen(valid_indices, 'train', augmentation_transforms=augmentation_transforms, chunk_size=CHUNK_SIZE, target_sizes=input_sizes, ds_transforms=ds_transforms, processor_class=ra.LoadAndProcessFixedPysexCenteringRescaling)
valid_gen = load_data.buffered_gen_mp(augmented_data_gen_valid, buffer_size=1)
augmented_data_gen_test = ra.realtime_fixed_augmented_data_gen(test_indices, 'test', augmentation_transforms=augmentation_transforms, chunk_size=CHUNK_SIZE, target_sizes=input_sizes, ds_transforms=ds_transforms, processor_class=ra.LoadAndProcessFixedPysexCenteringRescaling)
test_gen = load_data.buffered_gen_mp(augmented_data_gen_test, buffer_size=1)
approx_num_chunks_valid = int(np.ceil(num_valid * len(augmentation_transforms) / float(CHUNK_SIZE)))
approx_num_chunks_test = int(np.ceil(num_test * len(augmentation_transforms) / float(CHUNK_SIZE)))
print "Approximately %d chunks for the validation set" % approx_num_chunks_valid
print "Approximately %d chunks for the test set" % approx_num_chunks_test
if DO_VALID:
print
print "VALIDATION SET"
print "Compute predictions"
predictions_list = []
start_time = time.time()
for e, (chunk_data, chunk_length) in enumerate(valid_gen):
print "Chunk %d" % (e + 1)
xs_chunk = chunk_data
# need to transpose the chunks to move the 'channels' dimension up
xs_chunk = [x_chunk.transpose(0, 3, 1, 2) for x_chunk in xs_chunk]
print " load data onto GPU"
for x_shared, x_chunk in zip(xs_shared, xs_chunk):
x_shared.set_value(x_chunk)
num_batches_chunk = int(np.ceil(chunk_length / float(BATCH_SIZE)))
# make predictions, don't forget to cute off the zeros at the end
predictions_chunk_list = []
for b in xrange(num_batches_chunk):
if b % 1000 == 0:
print " batch %d/%d" % (b + 1, num_batches_chunk)
predictions = compute_output(b)
predictions_chunk_list.append(predictions)
predictions_chunk = np.vstack(predictions_chunk_list)
predictions_chunk = predictions_chunk[:chunk_length] # cut off zeros / padding
print " compute average over transforms"
predictions_chunk_avg = predictions_chunk.reshape(-1, len(augmentation_transforms), 37).mean(1)
predictions_list.append(predictions_chunk_avg)
time_since_start = time.time() - start_time
print " %s since start" % load_data.hms(time_since_start)
all_predictions = np.vstack(predictions_list)
print "Write predictions to %s" % target_path_valid
load_data.save_gz(target_path_valid, all_predictions)
print "Evaluate"
rmse_valid = analysis['losses_valid'][-1]
rmse_augmented = np.sqrt(np.mean((y_valid - all_predictions)**2))
print " MSE (last iteration):\t%.6f" % rmse_valid
print " MSE (augmented):\t%.6f" % rmse_augmented
if DO_TEST:
print
print "TEST SET"
print "Compute predictions"
predictions_list = []
start_time = time.time()
for e, (chunk_data, chunk_length) in enumerate(test_gen):
print "Chunk %d" % (e + 1)
xs_chunk = chunk_data
# need to transpose the chunks to move the 'channels' dimension up
xs_chunk = [x_chunk.transpose(0, 3, 1, 2) for x_chunk in xs_chunk]
print " load data onto GPU"
for x_shared, x_chunk in zip(xs_shared, xs_chunk):
x_shared.set_value(x_chunk)
num_batches_chunk = int(np.ceil(chunk_length / float(BATCH_SIZE)))
# make predictions, don't forget to cute off the zeros at the end
predictions_chunk_list = []
for b in xrange(num_batches_chunk):
if b % 1000 == 0:
print " batch %d/%d" % (b + 1, num_batches_chunk)
predictions = compute_output(b)
predictions_chunk_list.append(predictions)
predictions_chunk = np.vstack(predictions_chunk_list)
predictions_chunk = predictions_chunk[:chunk_length] # cut off zeros / padding
print " compute average over transforms"
predictions_chunk_avg = predictions_chunk.reshape(-1, len(augmentation_transforms), 37).mean(1)
predictions_list.append(predictions_chunk_avg)
time_since_start = time.time() - start_time
print " %s since start" % load_data.hms(time_since_start)
all_predictions = np.vstack(predictions_list)
print "Write predictions to %s" % target_path_test
load_data.save_gz(target_path_test, all_predictions)
print "Done!"