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RGB-N | RGB-N-master/lib/compact_bilinear_pooling/compact_bilinear_pooling_test.py | from __future__ import absolute_import, division, print_function
import numpy as np
import tensorflow as tf
from compact_bilinear_pooling import compact_bilinear_pooling_layer
def bp(bottom1, bottom2, sum_pool=True):
assert(np.all(bottom1.shape[:3] == bottom2.shape[:3]))
batch_size, height, width = bottom1.shape[:3]
output_dim = bottom1.shape[-1] * bottom2.shape[-1]
bottom1_flat = bottom1.reshape((-1, bottom1.shape[-1]))
bottom2_flat = bottom2.reshape((-1, bottom2.shape[-1]))
output = np.empty((batch_size*height*width, output_dim), np.float32)
for n in range(len(output)):
output[n, ...] = np.outer(bottom1_flat[n], bottom2_flat[n]).reshape(-1)
output = output.reshape((batch_size, height, width, output_dim))
if sum_pool:
output = np.sum(output, axis=(1, 2))
return output
# Input and output tensors
# Input channels need to be specified for shape inference
input_dim1 = 2048
input_dim2 = 2048
output_dim = 16000
bottom1 = tf.placeholder(tf.float32, [None, None, None, input_dim1])
bottom2 = tf.placeholder(tf.float32, [None, None, None, input_dim2])
top = compact_bilinear_pooling_layer(bottom1, bottom2, output_dim, sum_pool=True)
grad = tf.gradients(top, [bottom1, bottom2])
def cbp(bottom1_value, bottom2_value):
sess = tf.get_default_session()
return sess.run(top, feed_dict={bottom1: bottom1_value,
bottom2: bottom2_value})
def cbp_with_grad(bottom1_value, bottom2_value):
sess = tf.get_default_session()
return sess.run([top]+grad, feed_dict={bottom1: bottom1_value,
bottom2: bottom2_value})
def test_kernel_approximation(batch_size, height, width):
print("Testing kernel approximation...")
# Input values
x = np.random.rand(batch_size, height, width, input_dim1).astype(np.float32)
y = np.random.rand(batch_size, height, width, input_dim2).astype(np.float32)
z = np.random.rand(batch_size, height, width, input_dim1).astype(np.float32)
w = np.random.rand(batch_size, height, width, input_dim2).astype(np.float32)
# Compact Bilinear Pooling results
cbp_xy = cbp(x, y)
cbp_zw = cbp(z, w)
# (Original) Bilinear Pooling results
bp_xy = bp(x, y)
bp_zw = bp(z, w)
# Check the kernel results of Compact Bilinear Pooling
# against Bilinear Pooling
cbp_kernel = np.sum(cbp_xy*cbp_zw, axis=1)
bp_kernel = np.sum(bp_xy*bp_zw, axis=1)
ratio = cbp_kernel / bp_kernel
print("ratio between Compact Bilinear Pooling (CBP) and Bilinear Pooling (BP):")
print(ratio)
assert(np.all(np.abs(ratio - 1) < 2e-2))
print("Passed.")
def test_large_input(batch_size, height, width):
print("Testing large input...")
# Input values
x = np.random.rand(batch_size, height, width, input_dim1).astype(np.float32)
y = np.random.rand(batch_size, height, width, input_dim2).astype(np.float32)
# Compact Bilinear Pooling results
_ = cbp_with_grad(x, y)
# Test passes iff no exception occurs.
print("Passed.")
def main():
sess = tf.InteractiveSession()
test_kernel_approximation(batch_size=2, height=3, width=4)
test_large_input(batch_size=64, height=14, width=14)
sess.close()
if __name__ == '__main__':
main()
| 3,295 | 34.44086 | 84 | py |
RGB-N | RGB-N-master/lib/compact_bilinear_pooling/__init__.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
| 248 | 34.571429 | 58 | py |
RGB-N | RGB-N-master/lib/compact_bilinear_pooling/sequential_fft/sequential_batch_fft_test.py | from __future__ import absolute_import, division, print_function
import tensorflow as tf
import numpy as np
from sequential_batch_fft_ops import sequential_batch_fft, sequential_batch_ifft
compute_size = 128
x = tf.placeholder(tf.complex64, [None, None])
x_128 = tf.placeholder(tf.complex128, [None, None])
# FFT
x_fft = sequential_batch_fft(x, compute_size)
x_fft_128 = sequential_batch_fft(x_128, compute_size)
x_fft_tf = tf.fft(x)
# IFFT
x_ifft = sequential_batch_ifft(x, compute_size)
x_ifft_128 = sequential_batch_ifft(x_128, compute_size)
x_ifft_tf = tf.ifft(x)
# Grads
gx_fft = tf.gradients(x_fft, x)[0]
gx_fft_128 = tf.gradients(x_fft_128, x_128)[0]
gx_fft_tf = tf.gradients(x_fft_tf, x)[0]
gx_ifft = tf.gradients(x_ifft, x)[0]
gx_ifft_128 = tf.gradients(x_ifft_128, x_128)[0]
gx_ifft_tf = tf.gradients(x_ifft_tf, x)[0]
def test_shape():
print("Testing shape...")
# Test Shape inference. Output shape should be
# the same as input shape
input_pl = tf.placeholder(tf.complex64, [1000, 16000])
output_fft = sequential_batch_fft(input_pl)
output_ifft = sequential_batch_ifft(input_pl)
g_fft = tf.gradients(output_fft, input_pl)[0]
g_ifft = tf.gradients(output_ifft, input_pl)[0]
assert(output_fft.get_shape() == input_pl.get_shape())
assert(output_ifft.get_shape() == input_pl.get_shape())
assert(g_fft.get_shape() == input_pl.get_shape())
assert(g_ifft.get_shape() == input_pl.get_shape())
print("Passed.")
def test_forward():
# Test forward and compare with tf.batch_fft and tf.batch_ifft
print("Testing forward...")
sess = tf.Session()
for dim in range(1000, 5000, 1000):
for batch_size in range(1, 10):
x_val = (np.random.randn(batch_size, dim) +
np.random.randn(batch_size, dim) * 1j).astype(np.complex64)
# Forward complex64
x_fft_val, x_ifft_tf_val = sess.run([x_fft, x_ifft], {x: x_val})
# Forward complex128
x_fft_128_val, x_ifft_128_val = sess.run([x_fft_128, x_ifft_128],
{x_128: x_val.astype(np.complex128)})
# Forward with reference tf.batch_fft and tf.batch_ifft
x_fft_tf_val, x_ifft_val = sess.run([x_fft_tf, x_ifft_tf], {x: x_val})
ref_sum_fft = np.sum(np.abs(x_fft_tf_val))
ref_sum_ifft = np.sum(np.abs(x_ifft_tf_val))
relative_diff_fft = np.sum(np.abs(x_fft_val - x_fft_tf_val)) / ref_sum_fft
relative_diff_ifft = np.sum(np.abs(x_ifft_val - x_ifft_tf_val)) / ref_sum_ifft
relative_diff_fft128 = np.sum(np.abs(x_fft_128_val - x_fft_tf_val)) / ref_sum_fft
relative_diff_ifft128 = np.sum(np.abs(x_ifft_128_val - x_ifft_tf_val)) / ref_sum_ifft
assert(relative_diff_fft < 1e-5)
assert(relative_diff_fft128 < 1e-5)
assert(relative_diff_ifft < 1e-5)
assert(relative_diff_ifft128 < 1e-5)
sess.close()
print("Passed.")
def test_gradient():
# Test Backward and compare with tf.batch_fft and tf.batch_ifft
print("Testing gradient...")
sess = tf.Session()
for dim in range(1000, 5000, 1000):
for batch_size in range(1, 10):
x_val = (np.random.randn(batch_size, dim) +
np.random.randn(batch_size, dim) * 1j).astype(np.complex64)
# Backward complex64
gx_fft_val, gx_ifft_tf_val = sess.run([gx_fft, gx_ifft], {x: x_val})
# Backward complex128
gx_fft_128_val, gx_ifft_128_val = sess.run([gx_fft_128, gx_ifft_128],
{x_128: x_val.astype(np.complex128)})
# Backward with reference tf.batch_fft and tf.batch_ifft
gx_fft_tf_val, gx_ifft_val = sess.run([gx_fft_tf, gx_ifft_tf], {x: x_val})
ref_sum_fft = np.sum(np.abs(gx_fft_tf_val))
ref_sum_ifft = np.sum(np.abs(gx_ifft_tf_val))
relative_diff_fft = np.sum(np.abs(gx_fft_val - gx_fft_tf_val)) / ref_sum_fft
relative_diff_ifft = np.sum(np.abs(gx_ifft_val - gx_ifft_tf_val)) / ref_sum_ifft
relative_diff_fft128 = np.sum(np.abs(gx_fft_128_val - gx_fft_tf_val)) / ref_sum_fft
relative_diff_ifft128 = np.sum(np.abs(gx_ifft_128_val - gx_ifft_tf_val)) / ref_sum_ifft
assert(relative_diff_fft < 1e-5)
assert(relative_diff_fft128 < 1e-5)
assert(relative_diff_ifft < 1e-5)
assert(relative_diff_ifft128 < 1e-5)
sess.close()
print("Passed.")
def test_large_input():
# Very large input size, where tf.batch_fft and tf.batch_ifft
# will run OOM
print("Testing large input...")
sess = tf.Session()
batch_size, dim = 64*16*16, 16000
print("Forwarding and Backwarding with input shape",
[batch_size, dim], "This may take a while...")
x_val = (np.random.randn(batch_size, dim) +
np.random.randn(batch_size, dim) * 1j).astype(np.complex64)
sess.run(tf.group(x_fft, x_ifft, gx_fft, gx_ifft), {x: x_val})
sess.close()
# Test passes iff no exception occurs.
print("Passed.")
if __name__ == "__main__":
test_shape()
test_forward()
test_gradient()
test_large_input()
| 5,280 | 38.706767 | 99 | py |
RGB-N | RGB-N-master/lib/compact_bilinear_pooling/sequential_fft/sequential_batch_fft_ops.py | from __future__ import absolute_import, division, print_function
import os.path as osp
import tensorflow as tf
from tensorflow.python.framework import ops
# load module
module = tf.load_op_library(osp.join(osp.dirname(__file__),
'build/sequential_batch_fft.so'))
sequential_batch_fft = module.sequential_batch_fft
sequential_batch_ifft = module.sequential_batch_ifft
# Shape registration is moved to C++ to be compatible with TensorFlow 1.0 API
# @tf.RegisterShape("SequentialBatchFFT")
# def _SequentialBatchFFTShape(op):
# return [op.inputs[0].get_shape()]
#
# @tf.RegisterShape("SequentialBatchIFFT")
# def _SequentialBatchIFFTShape(op):
# return [op.inputs[0].get_shape()]
@ops.RegisterGradient("SequentialBatchFFT")
def _SequentialBatchFFTGrad(op, grad):
if (grad.dtype == tf.complex64):
size = tf.cast(tf.shape(grad)[1], tf.float32)
return (sequential_batch_ifft(grad, op.get_attr("compute_size"))
* tf.complex(size, 0.))
else:
size = tf.cast(tf.shape(grad)[1], tf.float64)
return (sequential_batch_ifft(grad, op.get_attr("compute_size"))
* tf.complex(size, tf.zeros([], tf.float64)))
@ops.RegisterGradient("SequentialBatchIFFT")
def _SequentialBatchIFFTGrad(op, grad):
if (grad.dtype == tf.complex64):
rsize = 1. / tf.cast(tf.shape(grad)[1], tf.float32)
return (sequential_batch_fft(grad, op.get_attr("compute_size"))
* tf.complex(rsize, 0.))
else:
rsize = 1. / tf.cast(tf.shape(grad)[1], tf.float64)
return (sequential_batch_fft(grad, op.get_attr("compute_size"))
* tf.complex(rsize, tf.zeros([], tf.float64)))
| 1,700 | 36.8 | 77 | py |
RGB-N | RGB-N-master/lib/compact_bilinear_pooling/sequential_fft/__init__.py | from .sequential_batch_fft_ops import sequential_batch_fft, sequential_batch_ifft
| 82 | 40.5 | 81 | py |
RGB-N | RGB-N-master/lib/datasets/voc_eval.py | # --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import xml.etree.ElementTree as ET
import os
import pickle
import numpy as np
import pdb
def parse_rec(filename):
""" Parse a PASCAL VOC xml file """
tree = ET.parse(filename)
objects = []
for obj in tree.findall('object'):
obj_struct = {}
obj_struct['name'] = obj.find('name').text
obj_struct['pose'] = obj.find('pose').text
obj_struct['truncated'] = int(obj.find('truncated').text)
obj_struct['difficult'] = int(obj.find('difficult').text)
bbox = obj.find('bndbox')
obj_struct['bbox'] = [int(bbox.find('xmin').text),
int(bbox.find('ymin').text),
int(bbox.find('xmax').text),
int(bbox.find('ymax').text)]
objects.append(obj_struct)
return objects
def voc_ap(rec, prec, use_07_metric=False):
""" ap = voc_ap(rec, prec, [use_07_metric])
Compute VOC AP given precision and recall.
If use_07_metric is true, uses the
VOC 07 11 point method (default:False).
"""
if use_07_metric:
# 11 point metric
ap = 0.
for t in np.arange(0., 1.1, 0.1):
if np.sum(rec >= t) == 0:
p = 0
else:
p = np.max(prec[rec >= t])
ap = ap + p / 11.
else:
# correct AP calculation
# first append sentinel values at the end
mrec = np.concatenate(([0.], rec, [1.]))
mpre = np.concatenate(([0.], prec, [0.]))
# compute the precision envelope
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# to calculate area under PR curve, look for points
# where X axis (recall) changes value
i = np.where(mrec[1:] != mrec[:-1])[0]
# and sum (\Delta recall) * prec
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return ap
def parse_txt(fileline,classname):
#classes=('__background__','person_au','person_tp','airplane_tp','airplane_au','dog_tp','dog_au',
#'train_tp','train_au','bed_tp','bed_au','refrigerator_tp','refrigerator_au')
#classes=('__background__','person_au','person_tp','tv_tp','tv_au','airplane_tp','airplane_au','dog_tp','dog_au',
#'bench_tp','bench_au','train_tp','train_au','broccoli_tp','broccoli_au','kite_tp','kite_au','bed_tp','bed_au','refrigerator_tp','refrigerator_au','bowl_tp','bowl_au')
classes=('__background__', 'tamper','authentic')
classes=('authentic', 'tamper')
#classes=('__background__', # always index 0
#'splicing','removal','manipulation')
#classes=('__background__','person_au','tv_au','airplane_au','dog_au',
#'bench_au','train_au','broccoli_au','kite_au','bed_au','refrigerator_au','bowl_au')
class_to_ind = dict(list(zip(classes, list(range(len(classes))))))
num_objs = int(len(fileline.split(' ')[1:])/5)
objects=[]
obj={}
#print(fileline.split())
#pdb.set_trace()
#object['name']=fileline.split(" ")[0]
for i in range(num_objs):
obj['bbox']=[float(fileline.split(' ')[5*i+1]),
float(fileline.split(' ')[5*i+2]),
float(fileline.split(' ')[5*i+3]),
float(fileline.split(' ')[5*i+4])]
try:
obj['cls']=class_to_ind[fileline.split(' ')[5*i+5]]
except:
#pdb.set_trace()
obj['cls']=int(fileline.split(' ')[5*i+5])
#obj['bbox']=[int(fileline.split(' ')[(classname-1)*5+1]),
#int(fileline.split(' ')[(classname-1)*5+2]),
#int(fileline.split(' ')[(classname-1)*5+3]),
#int(fileline.split(' ')[(classname-1)*5+4])]
#obj['cls']=int(fileline.split(' ')[(classname-1)*5+5])
objects.append(obj.copy())
return objects
def voc_eval(detpath,
detpath2,
annopath,
imagesetfile,
classname,
cachedir,
ovthresh=0.5,
use_07_metric=False,
fuse=False):
"""rec, prec, ap = voc_eval(detpath,
annopath,
imagesetfile,
classname,
[ovthresh],
[use_07_metric])
Top level function that does the PASCAL VOC evaluation.
detpath: Path to detections
detpath.format(classname) should produce the detection results file.
annopath: Path to annotations
annopath.format(imagename) should be the xml annotations file.
imagesetfile: Text file containing the list of images, one image per line.
classname: Category name (duh)
cachedir: Directory for caching the annotations
[ovthresh]: Overlap threshold (default = 0.5)
[use_07_metric]: Whether to use VOC07's 11 point AP computation
(default False)
"""
# assumes detections are in detpath.format(classname)
# assumes annotations are in annopath.format(imagename)
# assumes imagesetfile is a text file with each line an image name
# cachedir caches the annotations in a pickle file
# first load gt
if not os.path.isdir(cachedir):
os.mkdir(cachedir)
cachefile = os.path.join(cachedir, 'annots.pkl')
# read list of images
with open(imagesetfile, 'r') as f:
lines = f.readlines()
imagenames = [x.strip() for x in lines]
if not os.path.isfile(cachefile):
# load annots
recs = {}
for i, imagename in enumerate(imagenames):
name=imagename.split(' ')[0]
recs[name] = parse_txt(imagename,classname)
#recs[imagename] = parse_rec(annopath.format(imagename))
if i % 100 == 0:
print('Reading annotation for {:d}/{:d}'.format(
i + 1, len(imagenames)))
# save
print('Saving cached annotations to {:s}'.format(cachefile))
#with open(cachefile, 'w') as f:
#pickle.dump(recs, f)
else:
# load
with open(cachefile, 'rb') as f:
try:
recs = pickle.load(f)
except:
recs = pickle.load(f, encoding='bytes')
# extract gt objects for this class
class_recs = {}
npos = 0
#pdb.set_trace()
for imagename in imagenames:
name=imagename.split(' ')[0]
R = [obj for obj in recs[name] if obj['cls'] == classname]
npos=npos+len(R)
bbox = np.array([x['bbox'] for x in R])
#difficult = np.array([x['difficult'] for x in R]).astype(np.bool)
det = [False] * len(R)
class_recs[name] = {'bbox': bbox,
#'difficult': difficult,
'det': det}
# read dets
detfile = detpath.format(classname)
detfile_n = detpath2.format(classname)
#print(detfile)
with open(detfile, 'r') as f:
lines = f.readlines()
if os.path.isfile(detfile_n):
with open(detfile_n, 'r') as f_n:
n_lines = f_n.readlines()
n_splitlines = [x.strip().split(' ') for x in n_lines]
#print(n_splitlines)
image_n = [x[0] for x in n_splitlines]
confidence_n = np.array([float(x[1]) for x in n_splitlines])
BB_n = np.array([[float(z) for z in x[2:]] for x in n_splitlines])
splitlines = [x.strip().split(' ') for x in lines]
image_ids = [x[0] for x in splitlines]
confidence = np.array([float(x[1]) for x in splitlines])
BB = np.array([[float(z) for z in x[2:]] for x in splitlines])
count=np.zeros(10)
noise_ct=0
select_final=np.array([True]*len(image_ids))
image_select=[]
if BB.shape[0] > 0 and fuse:
for k in range(len(image_ids)):
if image_ids[k] in image_select:
select_final[k]=False
continue
if image_ids[k] in image_n:
bb = BB[k, :].astype(float)
index=[i for i,ex in enumerate(image_n) if ex==image_ids[k]]
bb1 = BB_n[index, :].astype(float)
#print(index,bb1)
#pdb.set_trace()
c_n=confidence_n[index]
ix_min = np.maximum(bb1[:, 0], bb[0])
iy_min = np.maximum(bb1[:, 1], bb[1])
ix_max = np.minimum(bb1[:, 2], bb[2])
iy_max = np.minimum(bb1[:, 3], bb[3])
iw = np.maximum(ix_max - ix_min + 1., 0.)
ih = np.maximum(iy_max - iy_min + 1., 0.)
inters = iw * ih
# union
uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
(bb1[:, 2] - bb1[:, 0] + 1.) *
(bb1[:, 3] - bb1[:, 1] + 1.) - inters)
overlaps = inters / uni
ov_max = np.max(overlaps)
jmax = np.argmax(overlaps)
if ov_max>=0.5:
count[int(ov_max*10)]=count[int(ov_max*10)]+1
#print(confidence[k],c_n)
#confidence[k]=np.maximum(confidence[k],c_n[jmax])
confidence[k]=(confidence[k]+c_n[jmax])/2
#pdb.set_trace()
BB[k,:]=(confidence[k]*BB[k,:]+c_n[jmax]*bb1[jmax,:])/np.maximum(confidence[k]+c_n[jmax], np.finfo(np.float64).eps)
image_select.append(image_ids[k])
#if confidence[k]<c_n[jmax]-0.5:
#BB[k,:]=bb1[jmax,:]
#print(image_ids[k],confidence[k],c_n[jmax])
elif ov_max<0.5 and ov_max>0.1:
count[int(ov_max*10)]=count[int(ov_max*10)]+1
image_select.append(image_ids[k])
#select_final[k]=False
#BB[k,:]=(confidence[k]*BB[k,:]+c_n[jmax]*bb1[jmax,:])/(confidence[k]+c_n[jmax])
#pass
#select_final[k]=False
#confidence[k]=0.7*confidence[k]+0.3*c_n[jmax]
#if confidence[k]<c_n[jmax]:
#BB[k,:]=bb1[jmax,:]
#print(image_ids[k],confidence[k],c_n[jmax])
#confidence[k]=confidence[k]*max(ov_max+0.2,0.6)
else:
count[int(ov_max*10)]=count[int(ov_max*10)]+1
select_final[k]=False
#confidence[k]=confidence[k]*0.9
#if confidence[k]<c_n[jmax]:
#BB[k,:]=bb1[jmax,:]
#confidence[k]=c_n[jmax]*0.9
for nk in range(len(image_n)):
if image_n[nk] not in image_ids:
noise_ct=noise_ct+1
#image_ids.append(image_n[nk])
#select_final.append(select_final,True)
#confidence=np.append(confidence,confidence_n[nk])
#BB=np.vstack((BB,BB_n[nk,:]))
print('rgb no overlap: {:s}'.format(count))
print('noise no overlap: {:d}'.format(noise_ct))
image_ids=np.extract(select_final,image_ids)
confidence=np.extract(select_final,confidence)
BB=BB[select_final,:]
nd = len(image_ids)
#print(image_ids)
tp = np.zeros(nd)
fp = np.zeros(nd)
bb=[]
bb1=[]
#pdb.set_trace()
if BB.shape[0] > 0:
# sort by confidence
sorted_ind = np.argsort(-confidence)
sorted_scores = np.sort(-confidence)
BB = BB[sorted_ind, :]
image_ids = [image_ids[x] for x in sorted_ind]
#print(class_recs)
#print(sorted_ind)
# go down dets and mark TPs and FPs
for d in range(nd):
#print(bb1)
R = class_recs[image_ids[d]]
bb = BB[d, :].astype(float)
if fuse:
if image_ids[d] in image_n:
index=[i for i,ex in enumerate(image_n) if ex==image_ids[d]]
bb1 = BB_n[index, :].astype(float)
ix_min = np.maximum(bb1[:, 0], bb[0])
iy_min = np.maximum(bb1[:, 1], bb[1])
ix_max = np.minimum(bb1[:, 2], bb[2])
iy_max = np.minimum(bb1[:, 3], bb[3])
iw_n = np.maximum(ix_max - ix_min + 1., 0.)
ih_n = np.maximum(iy_max - iy_min + 1., 0.)
inters_n = iw_n * ih_n
# union
un = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
(bb1[:, 2] - bb1[:, 0] + 1.) *
(bb1[:, 3] - bb1[:, 1] + 1.) - inters_n)
overlaps_n = inters_n / un
ov_max_n = np.max(overlaps_n)
ovmax = -np.inf
BBGT = R['bbox'].astype(float)
#print(BBGT)
#pdb.set_trace()
if BBGT.size > 0:
# compute overlaps
# intersection
#print(BBGT)
#print(bb)
ixmin = np.maximum(BBGT[:, 0], bb[0])
iymin = np.maximum(BBGT[:, 1], bb[1])
ixmax = np.minimum(BBGT[:, 2], bb[2])
iymax = np.minimum(BBGT[:, 3], bb[3])
iw = np.maximum(ixmax - ixmin + 1., 0.)
ih = np.maximum(iymax - iymin + 1., 0.)
inters = iw * ih
# union
uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
(BBGT[:, 2] - BBGT[:, 0] + 1.) *
(BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)
overlaps = inters / uni
ovmax = np.max(overlaps)
jmax = np.argmax(overlaps)
print("overlap:")
print(overlaps)
if ovmax > ovthresh:
#print(R['det'][jmax])
#if not R['difficult'][jmax]:
if not R['det'][jmax]:
#print(R['det'][jmax])
tp[d] = 1.
R['det'][jmax] = 1
else:
fp[d] = 1.
else:
print('fp:{:s}'.format(image_ids[d]))
if fuse:
print('score:{:f}, ovmax:{:f}'.format(-sorted_scores[d],ov_max_n))
fp[d] = 1.
# compute precision recall
fp = np.cumsum(fp)
tp = np.cumsum(tp)
rec = tp / float(npos)
# avoid divide by zero in case the first detection matches a difficult
# ground truth
prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
ap = voc_ap(rec, prec, use_07_metric)
return rec, prec, ap | 13,267 | 34.100529 | 173 | py |
RGB-N | RGB-N-master/lib/datasets/dist_fake.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from datasets.imdb import imdb
import datasets.ds_utils as ds_utils
import numpy as np
import scipy.sparse
import scipy.io as sio
import utils.cython_bbox
import pickle
import subprocess
import uuid
import pdb
from .voc_eval import voc_eval
from model.config import cfg
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
class dist_fake(imdb):
def __init__(self, image_set, year, dist_path=None):
imdb.__init__(self, image_set)
self._year = year
self._image_set = image_set.split('dist_')[1]
self._dist_path = self._get_default_path() if dist_path is None \
else dist_path
self._data_path=self._dist_path
self._classes = ('__background__', # always index 0
'tamper','authentic')
self._classes = ('authentic', # always index 0
'tamper')
#self.classes =('authentic', # always index 0
#'splicing','removal')
self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes)))))
self._image_ext = {'.png','.jpg','.tif','.bmp','.JPG'}
self._image_index = self._load_image_set_index()
# Default to roidb handler
self._roidb_handler = self.gt_roidb
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(os.path.splitext(self._image_index[i].split(' ')[0])[0])
def image_path_from_index(self, index):
"""
Construct an image path from the image's "index" identifier.
"""
for ext in self._image_ext:
#image_path = os.path.join('/home-3/pengzhou@umd.edu/work/xintong/medifor/portrait/test_data',
#index + ext)
image_path = os.path.join(self._data_path,
index + ext)
image_path1=os.path.join('/home-3/pengzhou@umd.edu/work/pengzhou/dataset/NC2016_Test0613',
index + ext)
if os.path.isfile(image_path):
return image_path
elif os.path.isfile(image_path1):
return image_path1
else:
continue
assert os.path.isfile(image_path) and os.path.isfile(image_path1), \
'Path does not exist: {}'.format(image_path)
return image_path
def _load_image_set_index(self):
"""
Load the indexes listed in this dataset's image set file.
"""
# Example path to image set file:
# self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt
image_set_file = os.path.join(self._data_path,
self._image_set + '.txt')
assert os.path.exists(image_set_file), \
'Path does not exist: {}'.format(image_set_file)
with open(image_set_file) as f:
image_index = [x.strip() for x in f.readlines()]
#print(image_index)
return image_index
def _get_default_path(self):
"""
Return the default path where PASCAL VOC is expected to be installed.
"""
return os.path.join(cfg.DATA_DIR, 'NC2016_Test0613')
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
try:
roidb = pickle.load(fid)
except:
roidb = pickle.load(fid, encoding='bytes')
print('{} gt roidb loaded from {}'.format(self.name, cache_file))
return roidb
gt_roidb = [self.roidb_gt(index)
for index in self.image_index]
with open(cache_file, 'wb') as fid:
pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL)
print('wrote gt roidb to {}'.format(cache_file))
return gt_roidb
def rpn_roidb(self):
if int(self._year) == 2007 or self._image_set != 'test':
gt_roidb = self.gt_roidb()
rpn_roidb = self._load_rpn_roidb(gt_roidb)
roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)
else:
roidb = self._load_rpn_roidb(None)
return roidb
def roidb_gt(self,image_id):
num_objs = int(len(image_id.split(' ')[1:])/5)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix in range(num_objs):
bbox = image_id.split(' ')[ix*5+1:ix*5+5]
# Make pixel indexes 0-based
x1 = float(bbox[0])
y1 = float(bbox[1])
x2 = float(bbox[2])
y2 = float(bbox[3])
if x1<0:
x1=0
if y1<0:
y1=0
try:
cls=self._class_to_ind[image_id.split(' ')[ix*5+5]]
except:
if int(image_id.split(' ')[ix*5+5])==0:
print('authentic')
cls=2
else:
cls = int(image_id.split(' ')[ix*5+5])
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 ) * (y2 - y1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'JPGed':False,
'noised':False,
'seg_areas': seg_areas}
def _load_rpn_roidb(self, gt_roidb):
filename = self.config['rpn_file']
print('loading {}'.format(filename))
assert os.path.exists(filename), \
'rpn data not found at: {}'.format(filename)
with open(filename, 'rb') as f:
box_list = pickle.load(f)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def _load_pascal_annotation(self, index):
"""
Load image and bounding boxes info from XML file in the PASCAL VOC
format.
"""
filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
tree = ET.parse(filename)
objs = tree.findall('object')
if not self.config['use_diff']:
# Exclude the samples labeled as difficult
non_diff_objs = [
obj for obj in objs if int(obj.find('difficult').text) == 0]
# if len(non_diff_objs) != len(objs):
# print 'Removed {} difficult objects'.format(
# len(objs) - len(non_diff_objs))
objs = non_diff_objs
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
bbox = obj.find('bndbox')
# Make pixel indexes 0-based
x1 = float(bbox.find('xmin').text) - 1
y1 = float(bbox.find('ymin').text) - 1
x2 = float(bbox.find('xmax').text) - 1
y2 = float(bbox.find('ymax').text) - 1
cls = self._class_to_ind[obj.find('name').text.lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'seg_areas': seg_areas}
def _get_comp_id(self):
comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt']
else self._comp_id)
return comp_id
def _get_voc_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'nist_' + self._image_set + '_{:s}.txt'
path = os.path.join(
'.',
filename)
return path
def _get_voc_noise_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'nist_' + self._image_set + '_{:s}_noise.txt'
path = os.path.join(
'.',
filename)
return path
def _write_voc_results_file(self, all_boxes):
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print('Writing {} VOC results file'.format(cls))
filename = self._get_voc_results_file_template().format(cls)
print(filename)
with open(filename, 'w') as f:
for im_ind, index in enumerate(self.image_index):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
#pdb.set_trace()
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.format(index.split(' ')[0], dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
#pdb.set_trace()
def _do_python_eval(self, output_dir='output'):
annopath = os.path.join(
self._dist_path,
'coco_multi' ,
'Annotations',
'{:s}.xml')
imagesetfile = os.path.join(
self._dist_path,
self._image_set + '.txt')
cachedir = os.path.join(self._dist_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
#use_07_metric = True if int(self._year) < 2010 else False
use_07_metric = False
print('dist metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(self._classes):
if cls == '__background__' or cls == self.classes[0]:
cls_ind=0
continue
else:
cls_ind=self._class_to_ind[cls]
#elif cls=='median_filtering':
#cls_ind=3
#continue
filename = self._get_voc_results_file_template().format(cls)
filename2 = self._get_voc_noise_results_file_template().format(cls)
print(cls_ind)
rec, prec, ap = voc_eval(
filename,filename2, annopath, imagesetfile, cls_ind, cachedir, ovthresh=0.5,
use_07_metric=use_07_metric,fuse=False)
aps += [ap]
print(('AP for {} = {:.4f},recall = {:.4f}, precision = {:.4f}'.format(cls, ap,rec[-1],prec[-1])))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
fig=plt.figure()
plt.plot(rec,prec)
fig.suptitle('PR curve for {} detection'.format(cls),fontsize=20)
plt.xlabel('recall',fontsize=15)
plt.xlim((0,1.0))
plt.ylim((0,1.0))
plt.ylabel('precision',fontsize=15)
fig.savefig('{}.jpg'.format(cls))
print(('Mean AP = {:.4f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print(('{:.3f}'.format(ap)))
print(('{:.3f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def _do_matlab_eval(self, output_dir='output'):
print('-----------------------------------------------------')
print('Computing results with the official MATLAB eval code.')
print('-----------------------------------------------------')
path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',
'VOCdevkit-matlab-wrapper')
cmd = 'cd {} && '.format(path)
cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
cmd += '-r "dbstop if error; '
cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \
.format(self._devkit_path, self._get_comp_id(),
self._image_set, output_dir)
print(('Running:\n{}'.format(cmd)))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(all_boxes)
self._do_python_eval(output_dir)
#if self.config['matlab_eval']:
#self._do_matlab_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
#os.remove(filename)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
if __name__ == '__main__':
from datasets.dist_fake import dist_fake
d = dist_fake('trainval', '2007')
res = d.roidb
from IPython import embed;
embed()
| 13,392 | 34.619681 | 104 | py |
RGB-N | RGB-N-master/lib/datasets/pascal_voc.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Peng Zhou
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from datasets.imdb import imdb
import datasets.ds_utils as ds_utils
import xml.etree.ElementTree as ET
import numpy as np
import scipy.sparse
import scipy.io as sio
import utils.cython_bbox
import pickle
import subprocess
import uuid
from .voc_eval import voc_eval
from model.config import cfg
class pascal_voc(imdb):
def __init__(self, image_set, year, devkit_path=None):
imdb.__init__(self, 'voc_' + year + '_' + image_set)
self._year = year
self._image_set = image_set
self._devkit_path = self._get_default_path() if devkit_path is None \
else devkit_path
self._data_path = os.path.join(self._devkit_path, 'VOC' + self._year)
self._classes = ('__background__', # always index 0
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')
self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes)))))
self._image_ext = '.jpg'
self._image_index = self._load_image_set_index()
# Default to roidb handler
self._roidb_handler = self.gt_roidb
self._salt = str(uuid.uuid4())
self._comp_id = 'comp4'
# PASCAL specific config options
self.config = {'cleanup': True,
'use_salt': True,
'use_diff': False,
'matlab_eval': False,
'rpn_file': None}
assert os.path.exists(self._devkit_path), \
'VOCdevkit path does not exist: {}'.format(self._devkit_path)
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(self._image_index[i])
def image_path_from_index(self, index):
"""
Construct an image path from the image's "index" identifier.
"""
image_path = os.path.join(self._data_path, 'JPEGImages',
index + self._image_ext)
assert os.path.exists(image_path), \
'Path does not exist: {}'.format(image_path)
return image_path
def _load_image_set_index(self):
"""
Load the indexes listed in this dataset's image set file.
"""
# Example path to image set file:
# self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt
image_set_file = os.path.join(self._data_path, 'ImageSets', 'Main',
self._image_set + '.txt')
assert os.path.exists(image_set_file), \
'Path does not exist: {}'.format(image_set_file)
with open(image_set_file) as f:
image_index = [x.strip() for x in f.readlines()]
return image_index
def _get_default_path(self):
"""
Return the default path where PASCAL VOC is expected to be installed.
"""
return os.path.join(cfg.DATA_DIR, 'VOCdevkit' + self._year)
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
try:
roidb = pickle.load(fid)
except:
roidb = pickle.load(fid, encoding='bytes')
print('{} gt roidb loaded from {}'.format(self.name, cache_file))
return roidb
gt_roidb = [self._load_pascal_annotation(index)
for index in self.image_index]
with open(cache_file, 'wb') as fid:
pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL)
print('wrote gt roidb to {}'.format(cache_file))
return gt_roidb
def rpn_roidb(self):
if int(self._year) == 2007 or self._image_set != 'test':
gt_roidb = self.gt_roidb()
rpn_roidb = self._load_rpn_roidb(gt_roidb)
roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)
else:
roidb = self._load_rpn_roidb(None)
return roidb
def _load_rpn_roidb(self, gt_roidb):
filename = self.config['rpn_file']
print('loading {}'.format(filename))
assert os.path.exists(filename), \
'rpn data not found at: {}'.format(filename)
with open(filename, 'rb') as f:
box_list = pickle.load(f)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def _load_pascal_annotation(self, index):
"""
Load image and bounding boxes info from XML file in the PASCAL VOC
format.
"""
filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
tree = ET.parse(filename)
objs = tree.findall('object')
if not self.config['use_diff']:
# Exclude the samples labeled as difficult
non_diff_objs = [
obj for obj in objs if int(obj.find('difficult').text) == 0]
# if len(non_diff_objs) != len(objs):
# print 'Removed {} difficult objects'.format(
# len(objs) - len(non_diff_objs))
objs = non_diff_objs
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
bbox = obj.find('bndbox')
# Make pixel indexes 0-based
x1 = float(bbox.find('xmin').text) - 1
y1 = float(bbox.find('ymin').text) - 1
x2 = float(bbox.find('xmax').text) - 1
y2 = float(bbox.find('ymax').text) - 1
cls = self._class_to_ind[obj.find('name').text.lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'seg_areas': seg_areas}
def _get_comp_id(self):
comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt']
else self._comp_id)
return comp_id
def _get_voc_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = self._get_comp_id() + '_det_' + self._image_set + '_{:s}.txt'
path = os.path.join(
self._devkit_path,
'results',
'VOC' + self._year,
'Main',
filename)
return path
def _write_voc_results_file(self, all_boxes):
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print('Writing {} VOC results file'.format(cls))
filename = self._get_voc_results_file_template().format(cls)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(self.image_index):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index, dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
def _do_python_eval(self, output_dir='output'):
annopath = os.path.join(
self._devkit_path,
'VOC' + self._year,
'Annotations',
'{:s}.xml')
imagesetfile = os.path.join(
self._devkit_path,
'VOC' + self._year,
'ImageSets',
'Main',
self._image_set + '.txt')
cachedir = os.path.join(self._devkit_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = True if int(self._year) < 2010 else False
print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(self._classes):
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
rec, prec, ap = voc_eval(
filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
use_07_metric=use_07_metric)
aps += [ap]
print(('AP for {} = {:.4f}'.format(cls, ap)))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
print(('Mean AP = {:.4f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print(('{:.3f}'.format(ap)))
print(('{:.3f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def _do_matlab_eval(self, output_dir='output'):
print('-----------------------------------------------------')
print('Computing results with the official MATLAB eval code.')
print('-----------------------------------------------------')
path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',
'VOCdevkit-matlab-wrapper')
cmd = 'cd {} && '.format(path)
cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
cmd += '-r "dbstop if error; '
cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \
.format(self._devkit_path, self._get_comp_id(),
self._image_set, output_dir)
print(('Running:\n{}'.format(cmd)))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(all_boxes)
self._do_python_eval(output_dir)
if self.config['matlab_eval']:
self._do_matlab_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
os.remove(filename)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
if __name__ == '__main__':
from datasets.pascal_voc import pascal_voc
d = pascal_voc('trainval', '2007')
res = d.roidb
from IPython import embed;
embed()
| 11,180 | 35.301948 | 85 | py |
RGB-N | RGB-N-master/lib/datasets/imdb.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import os.path as osp
import PIL
from utils.cython_bbox import bbox_overlaps
import numpy as np
import scipy.sparse
from model.config import cfg
class imdb(object):
"""Image database."""
def __init__(self, name, classes=None):
self._name = name
self._num_classes = 0
if not classes:
self._classes = []
else:
self._classes = classes
self._image_index = []
self._obj_proposer = 'gt'
self._roidb = None
self._roidb_handler = self.default_roidb
# Use this dict for storing dataset specific config options
self.config = {}
@property
def name(self):
return self._name
@property
def num_classes(self):
return len(self._classes)
@property
def classes(self):
return self._classes
@property
def image_index(self):
return self._image_index
@property
def roidb_handler(self):
return self._roidb_handler
@roidb_handler.setter
def roidb_handler(self, val):
self._roidb_handler = val
def set_proposal_method(self, method):
method = eval('self.' + method + '_roidb')
self.roidb_handler = method
@property
def roidb(self):
# A roidb is a list of dictionaries, each with the following keys:
# boxes
# gt_overlaps
# gt_classes
# flipped
if self._roidb is not None:
return self._roidb
self._roidb = self.roidb_handler()
return self._roidb
@property
def cache_path(self):
cache_path = osp.abspath(osp.join(cfg.DATA_DIR, 'cache'))
if not os.path.exists(cache_path):
os.makedirs(cache_path)
return cache_path
@property
def num_images(self):
return len(self.image_index)
def image_path_at(self, i):
raise NotImplementedError
def default_roidb(self):
raise NotImplementedError
def evaluate_detections(self, all_boxes, output_dir=None):
"""
all_boxes is a list of length number-of-classes.
Each list element is a list of length number-of-images.
Each of those list elements is either an empty list []
or a numpy array of detection.
all_boxes[class][image] = [] or np.array of shape #dets x 5
"""
raise NotImplementedError
def _get_widths(self):
return [PIL.Image.open(self.image_path_at(i)).size[0]
for i in range(self.num_images)]
def append_flipped_images(self):
num_images = self.num_images
widths = self._get_widths()
for i in range(num_images):
boxes = self.roidb[i]['boxes'].copy()
oldx1 = boxes[:, 0].copy()
oldx2 = boxes[:, 2].copy()
#print(oldx2,oldx1,widths[i])
if oldx2>=widths[i]:
oldx2=widths[i]-1
boxes[:, 0] = widths[i] - oldx2 - 1
boxes[:, 2] = widths[i] - oldx1 - 1
#print(boxes)
assert (boxes[:, 2] >= boxes[:, 0]).all()
entry = {'boxes': boxes,
'gt_overlaps': self.roidb[i]['gt_overlaps'],
'gt_classes': self.roidb[i]['gt_classes'],
'flipped': True,
'JPGed':False,
'noised':False}
self.roidb.append(entry)
self._image_index = self._image_index * 2
def append_noise_images(self):
num_images = self.num_images
widths = self._get_widths()
for i in range(num_images):
boxes = self.roidb[i]['boxes'].copy()
flipped= self.roidb[i]['flipped']
jpg= self.roidb[i]['JPGed']
entry = {'boxes': boxes,
'gt_overlaps': self.roidb[i]['gt_overlaps'],
'gt_classes': self.roidb[i]['gt_classes'],
'flipped':flipped,
'JPGed':jpg,
'noised': True}
self.roidb.append(entry)
self._image_index = self._image_index * 2
def append_jpg_images(self):
num_images = self.num_images
widths = self._get_widths()
for i in range(num_images):
boxes = self.roidb[i]['boxes'].copy()
flipped= self.roidb[i]['flipped']
noised= self.roidb[i]['noised']
entry = {'boxes': boxes,
'gt_overlaps': self.roidb[i]['gt_overlaps'],
'gt_classes': self.roidb[i]['gt_classes'],
'flipped':flipped,
'JPGed':True,
'noised':noised}
self.roidb.append(entry)
self._image_index = self._image_index * 2
def evaluate_recall(self, candidate_boxes=None, thresholds=None,
area='all', limit=None):
"""Evaluate detection proposal recall metrics.
Returns:
results: dictionary of results with keys
'ar': average recall
'recalls': vector recalls at each IoU overlap threshold
'thresholds': vector of IoU overlap thresholds
'gt_overlaps': vector of all ground-truth overlaps
"""
# Record max overlap value for each gt box
# Return vector of overlap values
areas = {'all': 0, 'small': 1, 'medium': 2, 'large': 3,
'96-128': 4, '128-256': 5, '256-512': 6, '512-inf': 7}
area_ranges = [[0 ** 2, 1e5 ** 2], # all
[0 ** 2, 32 ** 2], # small
[32 ** 2, 96 ** 2], # medium
[96 ** 2, 1e5 ** 2], # large
[96 ** 2, 128 ** 2], # 96-128
[128 ** 2, 256 ** 2], # 128-256
[256 ** 2, 512 ** 2], # 256-512
[512 ** 2, 1e5 ** 2], # 512-inf
]
assert area in areas, 'unknown area range: {}'.format(area)
area_range = area_ranges[areas[area]]
gt_overlaps = np.zeros(0)
num_pos = 0
for i in range(self.num_images):
# Checking for max_overlaps == 1 avoids including crowd annotations
# (...pretty hacking :/)
max_gt_overlaps = self.roidb[i]['gt_overlaps'].toarray().max(axis=1)
gt_inds = np.where((self.roidb[i]['gt_classes'] > 0) &
(max_gt_overlaps == 1))[0]
gt_boxes = self.roidb[i]['boxes'][gt_inds, :]
gt_areas = self.roidb[i]['seg_areas'][gt_inds]
valid_gt_inds = np.where((gt_areas >= area_range[0]) &
(gt_areas <= area_range[1]))[0]
gt_boxes = gt_boxes[valid_gt_inds, :]
num_pos += len(valid_gt_inds)
if candidate_boxes is None:
# If candidate_boxes is not supplied, the default is to use the
# non-ground-truth boxes from this roidb
non_gt_inds = np.where(self.roidb[i]['gt_classes'] == 0)[0]
boxes = self.roidb[i]['boxes'][non_gt_inds, :]
else:
boxes = candidate_boxes[i]
if boxes.shape[0] == 0:
continue
if limit is not None and boxes.shape[0] > limit:
boxes = boxes[:limit, :]
overlaps = bbox_overlaps(boxes.astype(np.float),
gt_boxes.astype(np.float))
_gt_overlaps = np.zeros((gt_boxes.shape[0]))
for j in range(gt_boxes.shape[0]):
# find which proposal box maximally covers each gt box
argmax_overlaps = overlaps.argmax(axis=0)
# and get the iou amount of coverage for each gt box
max_overlaps = overlaps.max(axis=0)
# find which gt box is 'best' covered (i.e. 'best' = most iou)
gt_ind = max_overlaps.argmax()
gt_ovr = max_overlaps.max()
assert (gt_ovr >= 0)
# find the proposal box that covers the best covered gt box
box_ind = argmax_overlaps[gt_ind]
# record the iou coverage of this gt box
_gt_overlaps[j] = overlaps[box_ind, gt_ind]
assert (_gt_overlaps[j] == gt_ovr)
# mark the proposal box and the gt box as used
overlaps[box_ind, :] = -1
overlaps[:, gt_ind] = -1
# append recorded iou coverage level
gt_overlaps = np.hstack((gt_overlaps, _gt_overlaps))
gt_overlaps = np.sort(gt_overlaps)
if thresholds is None:
step = 0.05
thresholds = np.arange(0.5, 0.95 + 1e-5, step)
recalls = np.zeros_like(thresholds)
# compute recall for each iou threshold
for i, t in enumerate(thresholds):
recalls[i] = (gt_overlaps >= t).sum() / float(num_pos)
# ar = 2 * np.trapz(recalls, thresholds)
ar = recalls.mean()
return {'ar': ar, 'recalls': recalls, 'thresholds': thresholds,
'gt_overlaps': gt_overlaps}
def create_roidb_from_box_list(self, box_list, gt_roidb):
assert len(box_list) == self.num_images, \
'Number of boxes must match number of ground-truth images'
roidb = []
for i in range(self.num_images):
boxes = box_list[i]
num_boxes = boxes.shape[0]
overlaps = np.zeros((num_boxes, self.num_classes), dtype=np.float32)
if gt_roidb is not None and gt_roidb[i]['boxes'].size > 0:
gt_boxes = gt_roidb[i]['boxes']
gt_classes = gt_roidb[i]['gt_classes']
gt_overlaps = bbox_overlaps(boxes.astype(np.float),
gt_boxes.astype(np.float))
argmaxes = gt_overlaps.argmax(axis=1)
maxes = gt_overlaps.max(axis=1)
I = np.where(maxes > 0)[0]
overlaps[I, gt_classes[argmaxes[I]]] = maxes[I]
overlaps = scipy.sparse.csr_matrix(overlaps)
roidb.append({
'boxes': boxes,
'gt_classes': np.zeros((num_boxes,), dtype=np.int32),
'gt_overlaps': overlaps,
'flipped': False,
'JPGed':False,
'noised':False
#'seg_areas': np.zeros((num_boxes,), dtype=np.float32),
})
return roidb
@staticmethod
def merge_roidbs(a, b):
assert len(a) == len(b)
for i in range(len(a)):
a[i]['boxes'] = np.vstack((a[i]['boxes'], b[i]['boxes']))
a[i]['gt_classes'] = np.hstack((a[i]['gt_classes'],
b[i]['gt_classes']))
a[i]['gt_overlaps'] = scipy.sparse.vstack([a[i]['gt_overlaps'],
b[i]['gt_overlaps']])
# a[i]['seg_areas'] = np.hstack((a[i]['seg_areas'],
#b[i]['seg_areas']))
return a
def competition_mode(self, on):
"""Turn competition mode on or off."""
pass
| 10,309 | 33.481605 | 74 | py |
RGB-N | RGB-N-master/lib/datasets/factory.py | # --------------------------------------------------------
# Tensorflow RGB-N
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou
# --------------------------------------------------------
"""Factory method for easily getting imdbs by name."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
__sets = {}
from datasets.pascal_voc import pascal_voc
from datasets.coco import coco
from datasets.casia import casia
from datasets.dist_fake import dist_fake
from datasets.nist import nist
from datasets.dvmm import dvmm
from datasets.swapme import swapme
import numpy as np
# Set up voc_<year>_<split>
for year in ['2007', '2012']:
for split in ['train', 'val', 'trainval', 'test']:
name = 'voc_{}_{}'.format(year, split)
__sets[name] = (lambda split=split, year=year: pascal_voc(split, year))
# Set up coco_2014_<split>
for year in ['2014']:
for split in ['train', 'val', 'minival', 'valminusminival', 'trainval']:
name = 'coco_{}_{}'.format(year, split)
__sets[name] = (lambda split=split, year=year: coco(split, year))
# Set up coco_2015_<split>
for year in ['2015']:
for split in ['test', 'test-dev']:
name = 'COCO_{}_{}'.format(year, split)
__sets[name] = (lambda split=split, year=year: coco(split, year))
dvmm_path='/vulcan/scratch/pengzhou/dataset/4cam_splc'
for split in ['dist_train', 'dist_test']:
name = split
__sets[name] = (lambda split=split: dvmm(split,2007,dvmm_path))
dso_path='/vulcan/scratch/pengzhou/dataset/COVERAGE'
for split in ['dist_cover_train_single', 'dist_cover_test_single']:
name = split
__sets[name] = (lambda split=split: dist_fake(split,2007,dso_path))
nist_path='/vulcan/scratch/pengzhou/dataset/NC2016_Test0613'
for split in ['dist_NIST_train_new_2', 'dist_NIST_test_new_2']:
name = split
__sets[name] = (lambda split=split: nist(split,2007,nist_path))
casia_path='/vulcan/scratch/pengzhou/dataset/CASIA2'
#for split in ['casia_train_all_single', 'casia_test_all_1']:
for split in ['casia_train_all_single', 'casia_test_all_single']:
name = split
__sets[name] = (lambda split=split: casia(split,2007,casia_path))
coco_path='/vulcan/scratch/pengzhou/dataset/filter_tamper'
for split in ['coco_train_filter_single', 'coco_test_filter_single']:
name = split
__sets[name] = (lambda split=split: coco(split,2007,coco_path))
swapme_path='/home-3/pengzhou@umd.edu/work/xintong/medifor/images/dataset_1k_final'
for split in ['face_faceswap_rcnn_train_only', 'face_faceswap_rcnn_test']:
name = split
__sets[name] = (lambda split=split: swapme(split,2007,swapme_path))
def get_imdb(name):
"""Get an imdb (image database) by name."""
if name not in __sets:
raise KeyError('Unknown dataset: {}'.format(name))
return __sets[name]()
def list_imdbs():
"""List all registered imdbs."""
return list(__sets.keys())
| 2,928 | 34.719512 | 83 | py |
RGB-N | RGB-N-master/lib/datasets/casia.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from datasets.imdb import imdb
import datasets.ds_utils as ds_utils
import numpy as np
import scipy.sparse
import scipy.io as sio
import utils.cython_bbox
import pickle
import subprocess
import uuid
from .voc_eval import voc_eval
from model.config import cfg
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
class casia(imdb):
def __init__(self, image_set, year, dist_path=None):
imdb.__init__(self, image_set)
self._year = year
self._image_set = image_set.split('casia_')[1]
self._dist_path = self._get_default_path() if dist_path is None \
else dist_path
self._data_path=self._dist_path
self._classes = ('__background__', # always index 0
'tamper','authentic')
self._classes = ('authentic', # always index 0
'tamper')
self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes)))))
self._image_ext = {'.png','.jpg','.tif','.bmp','.JPG'}
self._image_index = self._load_image_set_index()
# Default to roidb handler
self._roidb_handler = self.gt_roidb
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(os.path.splitext(self._image_index[i].split(' ')[0])[0])
def image_path_from_index(self, index):
"""
Construct an image path from the image's "index" identifier.
"""
for ext in self._image_ext:
#image_path = os.path.join('/home-3/pengzhou@umd.edu/work/xintong/medifor/portrait/test_data',
#index + ext)
image_path = os.path.join(self._data_path,
index + ext)
image_path1=os.path.join('/home-3/pengzhou@umd.edu/work/pengzhou/dataset/cocostuff/coco/train2014',
index + ext)
if os.path.isfile(image_path):
return image_path
elif os.path.isfile(image_path1):
return image_path1
else:
continue
assert os.path.isfile(image_path) and os.path.isfile(image_path1), \
'Path does not exist: {}'.format(image_path)
return image_path
def _load_image_set_index(self):
"""
Load the indexes listed in this dataset's image set file.
"""
# Example path to image set file:
# self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt
image_set_file = os.path.join(self._data_path,
self._image_set + '.txt')
assert os.path.exists(image_set_file), \
'Path does not exist: {}'.format(image_set_file)
with open(image_set_file) as f:
image_index = [x.strip() for x in f.readlines()]
#print(image_index)
return image_index
def _get_default_path(self):
"""
Return the default path where PASCAL VOC is expected to be installed.
"""
return os.path.join(cfg.DATA_DIR, 'CASIA1')
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
try:
roidb = pickle.load(fid)
except:
roidb = pickle.load(fid, encoding='bytes')
print('{} gt roidb loaded from {}'.format(self.name, cache_file))
return roidb
gt_roidb = [self.roidb_gt(index)
for index in self.image_index]
with open(cache_file, 'wb') as fid:
pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL)
print('wrote gt roidb to {}'.format(cache_file))
return gt_roidb
def rpn_roidb(self):
if int(self._year) == 2007 or self._image_set != 'test':
gt_roidb = self.gt_roidb()
rpn_roidb = self._load_rpn_roidb(gt_roidb)
roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)
else:
roidb = self._load_rpn_roidb(None)
return roidb
def roidb_gt(self,image_id):
num_objs = int(len(image_id.split(' ')[1:])/5)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix in range(num_objs):
bbox = image_id.split(' ')[ix*5+1:ix*5+5]
# Make pixel indexes 0-based
x1 = float(bbox[0]) -1
y1 = float(bbox[1]) -1
x2 = float(bbox[2]) -1
y2 = float(bbox[3]) -1
if x1<0:
x1=0
if y1<0:
y1=0
try:
cls=self._class_to_ind[image_id.split(' ')[ix*5+5]]
except:
if int(image_id.split(' ')[ix*5+5])==0:
print('authentic')
#cls=2
else:
cls = int(image_id.split(' ')[ix*5+5])
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 ) * (y2 - y1)
#print(image_id)
#print(boxes)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'JPGed': False,
'noised': False,
'seg_areas': seg_areas}
def _load_rpn_roidb(self, gt_roidb):
filename = self.config['rpn_file']
print('loading {}'.format(filename))
assert os.path.exists(filename), \
'rpn data not found at: {}'.format(filename)
with open(filename, 'rb') as f:
box_list = pickle.load(f)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def _load_pascal_annotation(self, index):
"""
Load image and bounding boxes info from XML file in the PASCAL VOC
format.
"""
filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
tree = ET.parse(filename)
objs = tree.findall('object')
if not self.config['use_diff']:
# Exclude the samples labeled as difficult
non_diff_objs = [
obj for obj in objs if int(obj.find('difficult').text) == 0]
# if len(non_diff_objs) != len(objs):
# print 'Removed {} difficult objects'.format(
# len(objs) - len(non_diff_objs))
objs = non_diff_objs
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
bbox = obj.find('bndbox')
# Make pixel indexes 0-based
x1 = float(bbox.find('xmin').text) - 1
y1 = float(bbox.find('ymin').text) - 1
x2 = float(bbox.find('xmax').text) - 1
y2 = float(bbox.find('ymax').text) - 1
cls = self._class_to_ind[obj.find('name').text.lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'JPGed':False,
'noised':False,
'seg_areas': seg_areas}
def _get_comp_id(self):
comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt']
else self._comp_id)
return comp_id
def _get_voc_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'casia_' + self._image_set + '_{:s}.txt'
path = os.path.join(
'.',
filename)
return path
def _get_voc_noise_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'casia_' + self._image_set + '_{:s}_noise.txt'
path = os.path.join(
'.',
filename)
return path
def _write_voc_results_file(self, all_boxes):
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print('Writing {} VOC results file'.format(cls))
filename = self._get_voc_results_file_template().format(cls)
print(filename)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(self.image_index):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index.split(' ')[0], dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
def _do_python_eval(self, output_dir='output'):
annopath = os.path.join(
self._dist_path,
'coco_multi' ,
'Annotations',
'{:s}.xml')
imagesetfile = os.path.join(
self._dist_path,
self._image_set + '.txt')
cachedir = os.path.join(self._dist_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
#use_07_metric = True if int(self._year) < 2010 else False
use_07_metric = False
print('dist metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(self._classes):
if cls == '__background__' or cls == self.classes[0]:
cls_ind=0
continue
else:
cls_ind=self._class_to_ind[cls]
#elif cls=='median_filtering':
#cls_ind=3
#continue
filename = self._get_voc_results_file_template().format(cls)
filename2 = self._get_voc_noise_results_file_template().format(cls)
print(cls_ind)
rec, prec, ap = voc_eval(
filename,filename2, annopath, imagesetfile, cls_ind, cachedir, ovthresh=0.5,
use_07_metric=use_07_metric,fuse=False)
aps += [ap]
print(('AP for {} = {:.4f},recall = {:.4f}, precision = {:.4f}'.format(cls, ap,rec[-1],prec[-1])))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
fig=plt.figure()
plt.plot(rec,prec)
fig.suptitle('PR curve for {} detection'.format(cls),fontsize=20)
plt.xlabel('recall',fontsize=15)
plt.xlim((0,1.0))
plt.ylim((0,1.0))
plt.ylabel('precision',fontsize=15)
fig.savefig('{}.jpg'.format(cls))
print(('Mean AP = {:.4f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print(('{:.3f}'.format(ap)))
print(('{:.3f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def _do_matlab_eval(self, output_dir='output'):
print('-----------------------------------------------------')
print('Computing results with the official MATLAB eval code.')
print('-----------------------------------------------------')
path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',
'VOCdevkit-matlab-wrapper')
cmd = 'cd {} && '.format(path)
cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
cmd += '-r "dbstop if error; '
cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \
.format(self._devkit_path, self._get_comp_id(),
self._image_set, output_dir)
print(('Running:\n{}'.format(cmd)))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(all_boxes)
self._do_python_eval(output_dir)
#if self.config['matlab_eval']:
#self._do_matlab_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
#os.remove(filename)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
if __name__ == '__main__':
from datasets.casia import casia
d = casia('trainval', '2007')
res = d.roidb
from IPython import embed;
embed()
| 13,348 | 34.597333 | 105 | py |
RGB-N | RGB-N-master/lib/datasets/swapme.py | # --------------------------------------------------------
# Tensorflow RGB-N
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from datasets.imdb import imdb
import datasets.ds_utils as ds_utils
import numpy as np
import scipy.sparse
import scipy.io as sio
import utils.cython_bbox
import pickle
import subprocess
import uuid
from .voc_eval import voc_eval
from model.config import cfg
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
class swapme(imdb):
def __init__(self, image_set, year, dist_path=None):
imdb.__init__(self, image_set)
self._year = year
self._image_set = image_set.split('face_')[1]
self._dist_path = self._get_default_path() if dist_path is None \
else dist_path
self._data_path=self._dist_path
self._classes = ('__background__', # always index 0
'tamper','authentic')
#self._classes = ('authentic', # always index 0
#'tamper')
self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes)))))
self._image_ext = {'.png','.jpg','.tif','.bmp','.JPG'}
self._image_index = self._load_image_set_index()
# Default to roidb handler
self._roidb_handler = self.gt_roidb
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(os.path.splitext(self._image_index[i].split(' ')[0])[0])
def image_path_from_index(self, index):
"""
Construct an image path from the image's "index" identifier.
"""
for ext in self._image_ext:
#image_path = os.path.join('/home-3/pengzhou@umd.edu/work/xintong/medifor/portrait/test_data',
#index + ext)
image_path = os.path.join(self._data_path,
index + ext)
image_path1=os.path.join('/home-3/pengzhou@umd.edu/work/pengzhou/dataset/DATA2',
index.split('/')[1] + ext)
if os.path.isfile(image_path):
return image_path
elif os.path.isfile(image_path1):
return image_path1
else:
continue
assert os.path.isfile(image_path) and os.path.isfile(image_path1), \
'Path does not exist: {}'.format(image_path)
return image_path
def _load_image_set_index(self):
"""
Load the indexes listed in this dataset's image set file.
"""
# Example path to image set file:
# self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt
image_set_file = os.path.join('/home-3/pengzhou@umd.edu/work/pengzhou/dataset',
self._image_set + '.txt')
assert os.path.exists(image_set_file), \
'Path does not exist: {}'.format(image_set_file)
with open(image_set_file) as f:
image_index = [x.strip() for x in f.readlines()]
#print(image_index)
return image_index
def _get_default_path(self):
"""
Return the default path where PASCAL VOC is expected to be installed.
"""
return os.path.join(cfg.DATA_DIR, 'swapme')
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
try:
roidb = pickle.load(fid)
except:
roidb = pickle.load(fid, encoding='bytes')
print('{} gt roidb loaded from {}'.format(self.name, cache_file))
return roidb
gt_roidb = [self.roidb_gt(index)
for index in self.image_index]
with open(cache_file, 'wb') as fid:
pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL)
print('wrote gt roidb to {}'.format(cache_file))
return gt_roidb
def rpn_roidb(self):
if int(self._year) == 2007 or self._image_set != 'test':
gt_roidb = self.gt_roidb()
rpn_roidb = self._load_rpn_roidb(gt_roidb)
roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)
else:
roidb = self._load_rpn_roidb(None)
return roidb
def roidb_gt(self,image_id):
num_objs = int(len(image_id.split(' ')[1:])/5)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix in range(num_objs):
bbox = image_id.split(' ')[ix*5+1:ix*5+5]
# Make pixel indexes 0-based
x1 = float(bbox[0]) -1
y1 = float(bbox[1]) -1
x2 = float(bbox[2]) -1
y2 = float(bbox[3]) -1
if x1<0:
x1=0
if y1<0:
y1=0
try:
cls=self._class_to_ind[image_id.split(' ')[ix*5+5]]
except:
if int(image_id.split(' ')[ix*5+5])==0:
print('authentic')
cls=2
else:
cls = int(image_id.split(' ')[ix*5+5])
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 ) * (y2 - y1)
#print(image_id)
#print(boxes)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'seg_areas': seg_areas}
def _load_rpn_roidb(self, gt_roidb):
filename = self.config['rpn_file']
print('loading {}'.format(filename))
assert os.path.exists(filename), \
'rpn data not found at: {}'.format(filename)
with open(filename, 'rb') as f:
box_list = pickle.load(f)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def _load_pascal_annotation(self, index):
"""
Load image and bounding boxes info from XML file in the PASCAL VOC
format.
"""
filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
tree = ET.parse(filename)
objs = tree.findall('object')
if not self.config['use_diff']:
# Exclude the samples labeled as difficult
non_diff_objs = [
obj for obj in objs if int(obj.find('difficult').text) == 0]
# if len(non_diff_objs) != len(objs):
# print 'Removed {} difficult objects'.format(
# len(objs) - len(non_diff_objs))
objs = non_diff_objs
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
bbox = obj.find('bndbox')
# Make pixel indexes 0-based
x1 = float(bbox.find('xmin').text) - 1
y1 = float(bbox.find('ymin').text) - 1
x2 = float(bbox.find('xmax').text) - 1
y2 = float(bbox.find('ymax').text) - 1
cls = self._class_to_ind[obj.find('name').text.lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'seg_areas': seg_areas}
def _get_comp_id(self):
comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt']
else self._comp_id)
return comp_id
def _get_voc_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'casia_' + self._image_set + '_{:s}.txt'
path = os.path.join(
'.',
filename)
return path
def _get_voc_noise_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'casia_' + self._image_set + '_{:s}_noise.txt'
path = os.path.join(
'.',
filename)
return path
def _write_voc_results_file(self, all_boxes):
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print('Writing {} VOC results file'.format(cls))
filename = self._get_voc_results_file_template().format(cls)
print(filename)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(self.image_index):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index.split(' ')[0], dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
def _do_python_eval(self, output_dir='output'):
annopath = os.path.join(
'/home-3/pengzhou@umd.edu/work/pengzhou/dataset',
'coco_multi' ,
'Annotations',
'{:s}.xml')
imagesetfile = os.path.join(
'/home-3/pengzhou@umd.edu/work/pengzhou/dataset',
self._image_set + '.txt')
cachedir = os.path.join('/home-3/pengzhou@umd.edu/work/pengzhou/dataset', 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
#use_07_metric = True if int(self._year) < 2010 else False
use_07_metric = False
print('dist metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(self._classes):
if cls == '__background__' or cls == self.classes[0]:
cls_ind=0
continue
else:
cls_ind=self._class_to_ind[cls]
#elif cls=='median_filtering':
#cls_ind=3
#continue
filename = self._get_voc_results_file_template().format(cls)
filename2 = self._get_voc_noise_results_file_template().format(cls)
print(cls_ind)
rec, prec, ap = voc_eval(
filename,filename2, annopath, imagesetfile, cls_ind, cachedir, ovthresh=0.5,
use_07_metric=use_07_metric,fuse=False)
aps += [ap]
print(('AP for {} = {:.4f},recall = {:.4f}, precision = {:.4f}'.format(cls, ap,rec[-1],prec[-1])))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
fig=plt.figure()
plt.plot(rec,prec)
fig.suptitle('PR curve for {} detection'.format(cls),fontsize=20)
plt.xlabel('recall',fontsize=15)
plt.xlim((0,1.0))
plt.ylim((0,1.0))
plt.ylabel('precision',fontsize=15)
fig.savefig('{}.jpg'.format(cls))
print(('Mean AP = {:.4f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print(('{:.3f}'.format(ap)))
print(('{:.3f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def _do_matlab_eval(self, output_dir='output'):
print('-----------------------------------------------------')
print('Computing results with the official MATLAB eval code.')
print('-----------------------------------------------------')
path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',
'VOCdevkit-matlab-wrapper')
cmd = 'cd {} && '.format(path)
cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
cmd += '-r "dbstop if error; '
cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \
.format(self._devkit_path, self._get_comp_id(),
self._image_set, output_dir)
print(('Running:\n{}'.format(cmd)))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(all_boxes)
self._do_python_eval(output_dir)
#if self.config['matlab_eval']:
#self._do_matlab_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
#os.remove(filename)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
if __name__ == '__main__':
from datasets.swapme import swapme
d = swapme('trainval', '2007')
res = d.roidb
from IPython import embed;
embed()
| 13,342 | 35.062162 | 104 | py |
RGB-N | RGB-N-master/lib/datasets/ds_utils.py | # --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
def unique_boxes(boxes, scale=1.0):
"""Return indices of unique boxes."""
v = np.array([1, 1e3, 1e6, 1e9])
hashes = np.round(boxes * scale).dot(v)
_, index = np.unique(hashes, return_index=True)
return np.sort(index)
def xywh_to_xyxy(boxes):
"""Convert [x y w h] box format to [x1 y1 x2 y2] format."""
return np.hstack((boxes[:, 0:2], boxes[:, 0:2] + boxes[:, 2:4] - 1))
def xyxy_to_xywh(boxes):
"""Convert [x1 y1 x2 y2] box format to [x y w h] format."""
return np.hstack((boxes[:, 0:2], boxes[:, 2:4] - boxes[:, 0:2] + 1))
def validate_boxes(boxes, width=0, height=0):
"""Check that a set of boxes are valid."""
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
assert (x1 >= 0).all()
assert (y1 >= 0).all()
assert (x2 >= x1).all()
assert (y2 >= y1).all()
assert (x2 < width).all()
assert (y2 < height).all()
def filter_small_boxes(boxes, min_size):
w = boxes[:, 2] - boxes[:, 0]
h = boxes[:, 3] - boxes[:, 1]
keep = np.where((w >= min_size) & (h > min_size))[0]
return keep
| 1,402 | 27.06 | 70 | py |
RGB-N | RGB-N-master/lib/datasets/__init__.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
| 248 | 34.571429 | 58 | py |
RGB-N | RGB-N-master/lib/datasets/coco.py | # --------------------------------------------------------
# Tensorflow RGB-N
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou, based on the code of Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from datasets.imdb import imdb
import datasets.ds_utils as ds_utils
import numpy as np
import scipy.sparse
import scipy.io as sio
import utils.cython_bbox
import pickle
import subprocess
import uuid
from .voc_eval import voc_eval
from model.config import cfg
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
class coco(imdb):
def __init__(self, image_set, year, dist_path=None):
imdb.__init__(self, image_set)
self._year = year
self._image_set = image_set.split('coco_')[1]
self._dist_path = self._get_default_path() if dist_path is None \
else dist_path
self._data_path=self._dist_path
#self._data_path = os.path.join(self._dist_path, image_set)
self._classes = ('__background__', # always index 0
'tamper','authentic')
self._classes = ('authentic', # always index 0
'tamper')
self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes)))))
#self._image_ext = {'.jpg','.tif'}
self._image_ext = {'.png','.jpg','.tif','.bmp','.JPG'}
self._image_index = self._load_image_set_index()
# Default to roidb handler
self._roidb_handler = self.gt_roidb
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(os.path.splitext(self._image_index[i].split(' ')[0])[0])
def image_path_from_index(self, index):
"""
Construct an image path from the image's "index" identifier.
"""
for ext in self._image_ext:
#image_path = os.path.join('/home-3/pengzhou@umd.edu/work/xintong/medifor/portrait/test_data',
#index + ext)
image_path = os.path.join(self._data_path,
index + ext)
image_path1=os.path.join('../dataset/train2014',
index + ext)
if os.path.isfile(image_path):
return image_path
elif os.path.isfile(image_path1):
return image_path1
else:
continue
assert os.path.isfile(image_path) and os.path.isfile(image_path1), \
'Path does not exist: {}'.format(image_path)
return image_path
def _load_image_set_index(self):
"""
Load the indexes listed in this dataset's image set file.
"""
# Example path to image set file:
# self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt
image_set_file = os.path.join(self._data_path,
self._image_set + '.txt')
assert os.path.exists(image_set_file), \
'Path does not exist: {}'.format(image_set_file)
with open(image_set_file) as f:
image_index = [x.strip() for x in f.readlines()]
return image_index
def _get_default_path(self):
"""
Return the default path where PASCAL VOC is expected to be installed.
"""
#return os.path.join(cfg.DATA_DIR, 'CASIA2')
return os.path.join(cfg.DATA_DIR, 'cocostuff/coco/splicing')
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
try:
roidb = pickle.load(fid)
except:
roidb = pickle.load(fid, encoding='bytes')
print('{} gt roidb loaded from {}'.format(self.name, cache_file))
return roidb
gt_roidb = [self.roidb_gt(index)
for index in self.image_index]
with open(cache_file, 'wb') as fid:
pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL)
print('wrote gt roidb to {}'.format(cache_file))
return gt_roidb
def rpn_roidb(self):
if int(self._year) == 2007 or self._image_set != 'test':
gt_roidb = self.gt_roidb()
rpn_roidb = self._load_rpn_roidb(gt_roidb)
roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)
else:
roidb = self._load_rpn_roidb(None)
return roidb
def roidb_gt(self,image_id):
num_objs = int(len(image_id.split(' ')[1:])/5)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix in range(num_objs):
bbox = image_id.split(' ')[ix*5+1:ix*5+5]
#print(bbox)
# Make pixel indexes 0-based
x1 = float(bbox[0]) -1
y1 = float(bbox[1]) -1
x2 = float(bbox[2]) -1
y2 = float(bbox[3]) -1
if x1<0:
x1=0
if y1<0:
y1=0
try:
cls=self._class_to_ind[image_id.split(' ')[ix*5+5]]
except:
cls = int(image_id.split(' ')[ix*5+5])
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 ) * (y2 - y1)
#print(image_id)
#print(boxes)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'JPGed': False,
'noised':False,
'seg_areas': seg_areas}
def _load_rpn_roidb(self, gt_roidb):
filename = self.config['rpn_file']
print('loading {}'.format(filename))
assert os.path.exists(filename), \
'rpn data not found at: {}'.format(filename)
with open(filename, 'rb') as f:
box_list = pickle.load(f)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def _load_pascal_annotation(self, index):
"""
Load image and bounding boxes info from XML file in the PASCAL VOC
format.
"""
filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
tree = ET.parse(filename)
objs = tree.findall('object')
if not self.config['use_diff']:
# Exclude the samples labeled as difficult
non_diff_objs = [
obj for obj in objs if int(obj.find('difficult').text) == 0]
# if len(non_diff_objs) != len(objs):
# print 'Removed {} difficult objects'.format(
# len(objs) - len(non_diff_objs))
objs = non_diff_objs
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
bbox = obj.find('bndbox')
# Make pixel indexes 0-based
x1 = float(bbox.find('xmin').text) - 1
y1 = float(bbox.find('ymin').text) - 1
x2 = float(bbox.find('xmax').text) - 1
y2 = float(bbox.find('ymax').text) - 1
cls = self._class_to_ind[obj.find('name').text.lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'seg_areas': seg_areas}
def _get_comp_id(self):
comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt']
else self._comp_id)
return comp_id
def _get_voc_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'coco_' + self._image_set + '_{:s}.txt'
path = os.path.join(
'.',
filename)
return path
def _get_voc_noise_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'coco_' + self._image_set + '_{:s}_noise.txt'
path = os.path.join(
'.',
filename)
return path
def _write_voc_results_file(self, all_boxes):
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print('Writing {} det results file'.format(cls))
filename = self._get_voc_results_file_template().format(cls)
print(filename)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(self.image_index):
dets = all_boxes[cls_ind][im_ind]
#print(dets)
#print(index)
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index.split(' ')[0], dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
def _do_python_eval(self, output_dir='output'):
annopath = os.path.join(
self._dist_path,
'coco_multi' ,
'Annotations',
'{:s}.xml')
imagesetfile = os.path.join(
self._dist_path,
self._image_set + '.txt')
cachedir = os.path.join(self._dist_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
#use_07_metric = True if int(self._year) < 2010 else False
use_07_metric = False
print('dist metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(self._classes):
if cls == '__background__' or cls == self.classes[0]:
cls_ind=0
continue
else:
cls_ind=self._class_to_ind[cls]
#elif cls=='median_filtering':
#cls_ind=3
#continue
filename = self._get_voc_results_file_template().format(cls)
filename2 = self._get_voc_noise_results_file_template().format(cls)
#print(cls_ind)
rec, prec, ap = voc_eval(
filename,filename2, annopath, imagesetfile, cls_ind, cachedir, ovthresh=0.5,
use_07_metric=use_07_metric,fuse=False)
aps += [ap]
print(('AP for {} = {:.4f},recall = {:.4f}, precision = {:.4f}'.format(cls, ap,rec[-1],prec[-1])))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
fig=plt.figure()
plt.plot(rec,prec)
fig.suptitle('PR curve for {} detection'.format(cls),fontsize=20)
plt.xlabel('recall',fontsize=15)
plt.xlim((0,1.0))
plt.ylim((0,1.0))
plt.ylabel('precision',fontsize=15)
fig.savefig('{}.png'.format(cls))
print(('Mean AP = {:.4f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print(('{:.3f}'.format(ap)))
print(('{:.3f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def _do_matlab_eval(self, output_dir='output'):
print('-----------------------------------------------------')
print('Computing results with the official MATLAB eval code.')
print('-----------------------------------------------------')
path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',
'VOCdevkit-matlab-wrapper')
cmd = 'cd {} && '.format(path)
cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
cmd += '-r "dbstop if error; '
cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \
.format(self._dist_path,
self._image_set, output_dir)
print(('Running:\n{}'.format(cmd)))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(all_boxes)
self._do_python_eval(output_dir)
#if self.config['matlab_eval']:
#self._do_matlab_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__' or cls == self.classes[0]:
continue
filename = self._get_voc_results_file_template().format(cls)
#os.remove(filename)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
if __name__ == '__main__':
from datasets.coco import coco
d = coco('train', '2007')
res = d.roidb
from IPython import embed;
embed()
| 13,334 | 34.65508 | 104 | py |
RGB-N | RGB-N-master/lib/datasets/nist.py | # --------------------------------------------------------
# Tensorflow RGB-N
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from datasets.imdb import imdb
import datasets.ds_utils as ds_utils
import numpy as np
import scipy.sparse
import scipy.io as sio
import utils.cython_bbox
import pickle
import subprocess
import uuid
import pdb
from .voc_eval import voc_eval
from model.config import cfg
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
class nist(imdb):
def __init__(self, image_set, year, dist_path=None):
imdb.__init__(self, image_set)
self._year = year
self._image_set = image_set.split('dist_')[1]
self._dist_path = self._get_default_path() if dist_path is None \
else dist_path
self._data_path=self._dist_path
self._classes = ('__background__', # always index 0
'tamper','authentic')
self._classes = ('__background__', # always index 0
'splicing','removal','manipulation')
self._classes = ('authentic', # always index 0
'tamper')
self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes)))))
self._image_ext = {'.png','.jpg','.tif','.bmp','.JPG'}
self._image_index = self._load_image_set_index()
# Default to roidb handler
self._roidb_handler = self.gt_roidb
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(os.path.splitext(self._image_index[i].split(' ')[0])[0])
def image_path_from_index(self, index):
"""
Construct an image path from the image's "index" identifier.
"""
for ext in self._image_ext:
#image_path = os.path.join('/home-3/pengzhou@umd.edu/work/xintong/medifor/portrait/test_data',
#index + ext)
image_path = os.path.join(self._data_path,
index + ext)
image_path1=os.path.join('/home-3/pengzhou@umd.edu/work/pengzhou/dataset/NC2016_Test0613',
index + ext)
if os.path.isfile(image_path):
return image_path
elif os.path.isfile(image_path1):
return image_path1
else:
continue
assert os.path.isfile(image_path) and os.path.isfile(image_path1), \
'Path does not exist: {}'.format(image_path)
return image_path
def _load_image_set_index(self):
"""
Load the indexes listed in this dataset's image set file.
"""
# Example path to image set file:
# self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt
image_set_file = os.path.join(self._data_path,
self._image_set + '.txt')
assert os.path.exists(image_set_file), \
'Path does not exist: {}'.format(image_set_file)
with open(image_set_file) as f:
image_index = [x.strip() for x in f.readlines()]
#print(image_index)
return image_index
def _get_default_path(self):
"""
Return the default path where PASCAL VOC is expected to be installed.
"""
return os.path.join(cfg.DATA_DIR, 'NC2016_Test0613')
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
try:
roidb = pickle.load(fid)
except:
roidb = pickle.load(fid, encoding='bytes')
print('{} gt roidb loaded from {}'.format(self.name, cache_file))
return roidb
gt_roidb = [self.roidb_gt(index)
for index in self.image_index]
with open(cache_file, 'wb') as fid:
pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL)
print('wrote gt roidb to {}'.format(cache_file))
return gt_roidb
def rpn_roidb(self):
if int(self._year) == 2007 or self._image_set != 'test':
gt_roidb = self.gt_roidb()
rpn_roidb = self._load_rpn_roidb(gt_roidb)
roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)
else:
roidb = self._load_rpn_roidb(None)
return roidb
def roidb_gt(self,image_id):
num_objs = int(len(image_id.split(' ')[1:])/5)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix in range(num_objs):
bbox = image_id.split(' ')[ix*5+1:ix*5+5]
# Make pixel indexes 0-based
x1 = float(bbox[0])
y1 = float(bbox[1])
x2 = float(bbox[2])
y2 = float(bbox[3])
if x1<0:
x1=0
if y1<0:
y1=0
try:
cls=self._class_to_ind[image_id.split(' ')[ix*5+5]]
except:
if int(image_id.split(' ')[ix*5+5])==0:
print('authentic')
cls=2
else:
cls = int(image_id.split(' ')[ix*5+5])
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 ) * (y2 - y1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'JPGed': False,
'noised':False,
'seg_areas': seg_areas}
def _load_rpn_roidb(self, gt_roidb):
filename = self.config['rpn_file']
print('loading {}'.format(filename))
assert os.path.exists(filename), \
'rpn data not found at: {}'.format(filename)
with open(filename, 'rb') as f:
box_list = pickle.load(f)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def _load_pascal_annotation(self, index):
"""
Load image and bounding boxes info from XML file in the PASCAL VOC
format.
"""
filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
tree = ET.parse(filename)
objs = tree.findall('object')
if not self.config['use_diff']:
# Exclude the samples labeled as difficult
non_diff_objs = [
obj for obj in objs if int(obj.find('difficult').text) == 0]
# if len(non_diff_objs) != len(objs):
# print 'Removed {} difficult objects'.format(
# len(objs) - len(non_diff_objs))
objs = non_diff_objs
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
bbox = obj.find('bndbox')
# Make pixel indexes 0-based
x1 = float(bbox.find('xmin').text) - 1
y1 = float(bbox.find('ymin').text) - 1
x2 = float(bbox.find('xmax').text) - 1
y2 = float(bbox.find('ymax').text) - 1
cls = self._class_to_ind[obj.find('name').text.lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'seg_areas': seg_areas}
def _get_comp_id(self):
comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt']
else self._comp_id)
return comp_id
def _get_voc_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'nist_' + self._image_set + '_{:s}.txt'
path = os.path.join(
'.',
filename)
return path
def _get_voc_noise_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'nist_' + self._image_set + '_{:s}_noise.txt'
path = os.path.join(
'.',
filename)
return path
def _write_voc_results_file(self, all_boxes):
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__' or cls == self.classes[0]:
continue
print('Writing {} VOC results file'.format(cls))
filename = self._get_voc_results_file_template().format(cls)
print(filename)
with open(filename, 'w') as f:
for im_ind, index in enumerate(self.image_index):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
#pdb.set_trace()
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.format(index.split(' ')[0], dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
#pdb.set_trace()
def _do_python_eval(self, output_dir='output'):
annopath = os.path.join(
self._dist_path,
'coco_multi' ,
'Annotations',
'{:s}.xml')
imagesetfile = os.path.join(
self._dist_path,
self._image_set + '.txt')
cachedir = os.path.join(self._dist_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
#use_07_metric = True if int(self._year) < 2010 else False
use_07_metric = False
print('dist metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(self._classes):
if cls == '__background__' or cls == self.classes[0]:
cls_ind=0
continue
else:
cls_ind=self._class_to_ind[cls]
#elif cls=='median_filtering':
#cls_ind=3
#continue
filename = self._get_voc_results_file_template().format(cls)
filename2 = self._get_voc_noise_results_file_template().format(cls)
print(cls_ind)
rec, prec, ap = voc_eval(
filename,filename2, annopath, imagesetfile, cls_ind, cachedir, ovthresh=0.5,
use_07_metric=use_07_metric,fuse=False)
aps += [ap]
print(('AP for {} = {:.4f},recall = {:.4f}, precision = {:.4f}'.format(cls, ap,rec[-1],prec[-1])))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
fig=plt.figure()
plt.plot(rec,prec)
fig.suptitle('PR curve for {} detection'.format(cls),fontsize=20)
plt.xlabel('recall',fontsize=15)
plt.xlim((0,1.0))
plt.ylim((0,1.0))
plt.ylabel('precision',fontsize=15)
fig.savefig('{}.png'.format(cls))
print(('Mean AP = {:.4f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print(('{:.3f}'.format(ap)))
print(('{:.3f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def _do_matlab_eval(self, output_dir='output'):
print('-----------------------------------------------------')
print('Computing results with the official MATLAB eval code.')
print('-----------------------------------------------------')
path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',
'VOCdevkit-matlab-wrapper')
cmd = 'cd {} && '.format(path)
cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
cmd += '-r "dbstop if error; '
cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \
.format(self._devkit_path, self._get_comp_id(),
self._image_set, output_dir)
print(('Running:\n{}'.format(cmd)))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(all_boxes)
self._do_python_eval(output_dir)
#if self.config['matlab_eval']:
#self._do_matlab_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__' or cls == self.classes[0]:
continue
filename = self._get_voc_results_file_template().format(cls)
#os.remove(filename)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
if __name__ == '__main__':
from datasets.dist_fake import dist_fake
d = nist('trainval', '2007')
res = d.roidb
from IPython import embed;
embed()
| 13,431 | 34.818667 | 104 | py |
RGB-N | RGB-N-master/lib/datasets/dvmm.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from datasets.imdb import imdb
import datasets.ds_utils as ds_utils
import numpy as np
import scipy.sparse
import scipy.io as sio
import utils.cython_bbox
import pickle
import subprocess
import uuid
import pdb
from .voc_eval import voc_eval
from model.config import cfg
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
class dvmm(imdb):
def __init__(self, image_set, year, dist_path=None):
imdb.__init__(self, image_set)
self._year = year
self._image_set = image_set.split('dist_')[1]
self._dist_path = self._get_default_path() if dist_path is None \
else dist_path
self._data_path=self._dist_path
self._classes = ('__background__', # always index 0
'tamper','authentic')
self._classes = ('authentic', # always index 0
'tamper')
#self.classes =('authentic', # always index 0
#'splicing','removal')
self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes)))))
self._image_ext = {'.png','.jpg','.tif','.bmp','.JPG'}
self._image_index = self._load_image_set_index()
# Default to roidb handler
self._roidb_handler = self.gt_roidb
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(os.path.splitext(self._image_index[i].split(' ')[0])[0])
def image_path_from_index(self, index):
"""
Construct an image path from the image's "index" identifier.
"""
for ext in self._image_ext:
#image_path = os.path.join('/home-3/pengzhou@umd.edu/work/xintong/medifor/portrait/test_data',
#index + ext)
image_path = os.path.join(self._data_path,
index + ext)
image_path1=os.path.join('/home-3/pengzhou@umd.edu/work/pengzhou/dataset/NC2016_Test0613',
index + ext)
if os.path.isfile(image_path):
return image_path
elif os.path.isfile(image_path1):
return image_path1
else:
continue
assert os.path.isfile(image_path) and os.path.isfile(image_path1), \
'Path does not exist: {}'.format(image_path)
return image_path
def _load_image_set_index(self):
"""
Load the indexes listed in this dataset's image set file.
"""
# Example path to image set file:
# self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt
image_set_file = os.path.join(self._data_path,
self._image_set + '.txt')
assert os.path.exists(image_set_file), \
'Path does not exist: {}'.format(image_set_file)
with open(image_set_file) as f:
image_index = [x.strip() for x in f.readlines()]
#print(image_index)
return image_index
def _get_default_path(self):
"""
Return the default path where PASCAL VOC is expected to be installed.
"""
return os.path.join(cfg.DATA_DIR, 'NC2016_Test0613')
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
try:
roidb = pickle.load(fid)
except:
roidb = pickle.load(fid, encoding='bytes')
print('{} gt roidb loaded from {}'.format(self.name, cache_file))
return roidb
gt_roidb = [self.roidb_gt(index)
for index in self.image_index]
with open(cache_file, 'wb') as fid:
pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL)
print('wrote gt roidb to {}'.format(cache_file))
return gt_roidb
def rpn_roidb(self):
if int(self._year) == 2007 or self._image_set != 'test':
gt_roidb = self.gt_roidb()
rpn_roidb = self._load_rpn_roidb(gt_roidb)
roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)
else:
roidb = self._load_rpn_roidb(None)
return roidb
def roidb_gt(self,image_id):
num_objs = int(len(image_id.split(' ')[1:])/5)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix in range(num_objs):
bbox = image_id.split(' ')[ix*5+1:ix*5+5]
# Make pixel indexes 0-based
x1 = float(bbox[0])
y1 = float(bbox[1])
x2 = float(bbox[2])
y2 = float(bbox[3])
if x1<0:
x1=0
if y1<0:
y1=0
try:
cls=self._class_to_ind[image_id.split(' ')[ix*5+5]]
except:
if int(image_id.split(' ')[ix*5+5])==0:
print('authentic')
#cls=2
else:
cls = int(image_id.split(' ')[ix*5+5])
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 ) * (y2 - y1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'seg_areas': seg_areas}
def _load_rpn_roidb(self, gt_roidb):
filename = self.config['rpn_file']
print('loading {}'.format(filename))
assert os.path.exists(filename), \
'rpn data not found at: {}'.format(filename)
with open(filename, 'rb') as f:
box_list = pickle.load(f)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def _load_pascal_annotation(self, index):
"""
Load image and bounding boxes info from XML file in the PASCAL VOC
format.
"""
filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
tree = ET.parse(filename)
objs = tree.findall('object')
if not self.config['use_diff']:
# Exclude the samples labeled as difficult
non_diff_objs = [
obj for obj in objs if int(obj.find('difficult').text) == 0]
# if len(non_diff_objs) != len(objs):
# print 'Removed {} difficult objects'.format(
# len(objs) - len(non_diff_objs))
objs = non_diff_objs
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
bbox = obj.find('bndbox')
# Make pixel indexes 0-based
x1 = float(bbox.find('xmin').text) - 1
y1 = float(bbox.find('ymin').text) - 1
x2 = float(bbox.find('xmax').text) - 1
y2 = float(bbox.find('ymax').text) - 1
cls = self._class_to_ind[obj.find('name').text.lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'seg_areas': seg_areas}
def _get_comp_id(self):
comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt']
else self._comp_id)
return comp_id
def _get_voc_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'dvmm_' + self._image_set + '_{:s}.txt'
path = os.path.join(
'.',
filename)
return path
def _get_voc_noise_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'dvmm_' + self._image_set + '_{:s}_noise.txt'
path = os.path.join(
'.',
filename)
return path
def _write_voc_results_file(self, all_boxes):
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print('Writing {} VOC results file'.format(cls))
filename = self._get_voc_results_file_template().format(cls)
print(filename)
with open(filename, 'w') as f:
for im_ind, index in enumerate(self.image_index):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
#pdb.set_trace()
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.format(index.split(' ')[0], dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
#pdb.set_trace()
def _do_python_eval(self, output_dir='output'):
annopath = os.path.join(
self._dist_path,
'coco_multi' ,
'Annotations',
'{:s}.xml')
imagesetfile = os.path.join(
self._dist_path,
self._image_set + '.txt')
cachedir = os.path.join(self._dist_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
#use_07_metric = True if int(self._year) < 2010 else False
use_07_metric = False
print('dist metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(self._classes):
if cls == '__background__' or cls == self.classes[0]:
cls_ind=0
continue
else:
cls_ind=self._class_to_ind[cls]
#elif cls=='median_filtering':
#cls_ind=3
#continue
filename = self._get_voc_results_file_template().format(cls)
filename2 = self._get_voc_noise_results_file_template().format(cls)
print(cls_ind)
rec, prec, ap = voc_eval(
filename,filename2, annopath, imagesetfile, cls_ind, cachedir, ovthresh=0.5,
use_07_metric=use_07_metric,fuse=False)
aps += [ap]
print(('AP for {} = {:.4f},recall = {:.4f}, precision = {:.4f}'.format(cls, ap,rec[-1],prec[-1])))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
fig=plt.figure()
plt.plot(rec,prec)
fig.suptitle('PR curve for {} detection'.format(cls),fontsize=20)
plt.xlabel('recall',fontsize=15)
plt.xlim((0,1.0))
plt.ylim((0,1.0))
plt.ylabel('precision',fontsize=15)
fig.savefig('{}.jpg'.format(cls))
print(('Mean AP = {:.4f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print(('{:.3f}'.format(ap)))
print(('{:.3f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def _do_matlab_eval(self, output_dir='output'):
print('-----------------------------------------------------')
print('Computing results with the official MATLAB eval code.')
print('-----------------------------------------------------')
path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',
'VOCdevkit-matlab-wrapper')
cmd = 'cd {} && '.format(path)
cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
cmd += '-r "dbstop if error; '
cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \
.format(self._devkit_path, self._get_comp_id(),
self._image_set, output_dir)
print(('Running:\n{}'.format(cmd)))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(all_boxes)
self._do_python_eval(output_dir)
#if self.config['matlab_eval']:
#self._do_matlab_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
#os.remove(filename)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
if __name__ == '__main__':
from datasets.dvmm import dvmm
d = dvmm('trainval', '2007')
res = d.roidb
from IPython import embed;
embed()
| 13,318 | 34.612299 | 104 | py |
RGB-N | RGB-N-master/lib/datasets/tools/mcg_munge.py | import os
import sys
"""Hacky tool to convert file system layout of MCG boxes downloaded from
http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/mcg/
so that it's consistent with those computed by Jan Hosang (see:
http://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-
computing/research/object-recognition-and-scene-understanding/how-
good-are-detection-proposals-really/)
NB: Boxes from the MCG website are in (y1, x1, y2, x2) order.
Boxes from Hosang et al. are in (x1, y1, x2, y2) order.
"""
def munge(src_dir):
# stored as: ./MCG-COCO-val2014-boxes/COCO_val2014_000000193401.mat
# want: ./MCG/mat/COCO_val2014_0/COCO_val2014_000000141/COCO_val2014_000000141334.mat
files = os.listdir(src_dir)
for fn in files:
base, ext = os.path.splitext(fn)
# first 14 chars / first 22 chars / all chars + .mat
# COCO_val2014_0/COCO_val2014_000000447/COCO_val2014_000000447991.mat
first = base[:14]
second = base[:22]
dst_dir = os.path.join('MCG', 'mat', first, second)
if not os.path.exists(dst_dir):
os.makedirs(dst_dir)
src = os.path.join(src_dir, fn)
dst = os.path.join(dst_dir, fn)
print 'MV: {} -> {}'.format(src, dst)
os.rename(src, dst)
if __name__ == '__main__':
# src_dir should look something like:
# src_dir = 'MCG-COCO-val2014-boxes'
src_dir = sys.argv[1]
munge(src_dir)
| 1,451 | 36.230769 | 94 | py |
RGB-N | RGB-N-master/lib/layer_utils/proposal_layer.py | # --------------------------------------------------------
# Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from model.config import cfg
from model.bbox_transform import bbox_transform_inv, clip_boxes
from model.nms_wrapper import nms
def proposal_layer(rpn_cls_prob, rpn_bbox_pred, im_info, cfg_key, _feat_stride, anchors, num_anchors):
"""A simplified version compared to fast/er RCNN
For details please see the technical report
"""
if type(cfg_key) == bytes:
cfg_key = cfg_key.decode('utf-8')
pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N
post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
nms_thresh = cfg[cfg_key].RPN_NMS_THRESH
im_info = im_info[0]
# Get the scores and bounding boxes
scores = rpn_cls_prob[:, :, :, num_anchors:]
rpn_bbox_pred = rpn_bbox_pred.reshape((-1, 4))
scores = scores.reshape((-1, 1))
proposals = bbox_transform_inv(anchors, rpn_bbox_pred)
proposals = clip_boxes(proposals, im_info[:2])
# Pick the top region proposals
order = scores.ravel().argsort()[::-1]
if pre_nms_topN > 0:
order = order[:pre_nms_topN]
proposals = proposals[order, :]
scores = scores[order]
# Non-maximal suppression
keep = nms(np.hstack((proposals, scores)), nms_thresh)
# Pick th top region proposals after NMS
if post_nms_topN > 0:
keep = keep[:post_nms_topN]
proposals = proposals[keep, :]
scores = scores[keep]
# Only support single image as input
batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))
return blob, scores
| 1,850 | 32.654545 | 102 | py |
RGB-N | RGB-N-master/lib/layer_utils/proposal_top_layer.py | # --------------------------------------------------------
# Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from model.config import cfg
from model.bbox_transform import bbox_transform_inv, clip_boxes
import numpy.random as npr
def proposal_top_layer(rpn_cls_prob, rpn_bbox_pred, im_info, _feat_stride, anchors, num_anchors):
"""A layer that just selects the top region proposals
without using non-maximal suppression,
For details please see the technical report
"""
rpn_top_n = cfg.TEST.RPN_TOP_N
im_info = im_info[0]
scores = rpn_cls_prob[:, :, :, num_anchors:]
rpn_bbox_pred = rpn_bbox_pred.reshape((-1, 4))
scores = scores.reshape((-1, 1))
length = scores.shape[0]
if length < rpn_top_n:
# Random selection, maybe unnecessary and loses good proposals
# But such case rarely happens
top_inds = npr.choice(length, size=rpn_top_n, replace=True)
else:
top_inds = scores.argsort(0)[::-1]
top_inds = top_inds[:rpn_top_n]
top_inds = top_inds.reshape(rpn_top_n, )
# Do the selection here
anchors = anchors[top_inds, :]
rpn_bbox_pred = rpn_bbox_pred[top_inds, :]
scores = scores[top_inds]
# Convert anchors into proposals via bbox transformations
proposals = bbox_transform_inv(anchors, rpn_bbox_pred)
# Clip predicted boxes to image
proposals = clip_boxes(proposals, im_info[:2])
# Output rois blob
# Our RPN implementation only supports a single input image, so all
# batch inds are 0
batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))
return blob, scores
| 1,868 | 32.981818 | 97 | py |
RGB-N | RGB-N-master/lib/layer_utils/generate_anchors.py | # --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Sean Bell
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
# Verify that we compute the same anchors as Shaoqing's matlab implementation:
#
# >> load output/rpn_cachedir/faster_rcnn_VOC2007_ZF_stage1_rpn/anchors.mat
# >> anchors
#
# anchors =
#
# -83 -39 100 56
# -175 -87 192 104
# -359 -183 376 200
# -55 -55 72 72
# -119 -119 136 136
# -247 -247 264 264
# -35 -79 52 96
# -79 -167 96 184
# -167 -343 184 360
# array([[ -83., -39., 100., 56.],
# [-175., -87., 192., 104.],
# [-359., -183., 376., 200.],
# [ -55., -55., 72., 72.],
# [-119., -119., 136., 136.],
# [-247., -247., 264., 264.],
# [ -35., -79., 52., 96.],
# [ -79., -167., 96., 184.],
# [-167., -343., 184., 360.]])
def generate_anchors(base_size=16, ratios=[0.5, 1, 2],
scales=2 ** np.arange(3, 6)):
"""
Generate anchor (reference) windows by enumerating aspect ratios X
scales wrt a reference (0, 0, 15, 15) window.
"""
base_anchor = np.array([1, 1, base_size, base_size]) - 1
ratio_anchors = _ratio_enum(base_anchor, ratios)
anchors = np.vstack([_scale_enum(ratio_anchors[i, :], scales)
for i in range(ratio_anchors.shape[0])])
return anchors
def _whctrs(anchor):
"""
Return width, height, x center, and y center for an anchor (window).
"""
w = anchor[2] - anchor[0] + 1
h = anchor[3] - anchor[1] + 1
x_ctr = anchor[0] + 0.5 * (w - 1)
y_ctr = anchor[1] + 0.5 * (h - 1)
return w, h, x_ctr, y_ctr
def _mkanchors(ws, hs, x_ctr, y_ctr):
"""
Given a vector of widths (ws) and heights (hs) around a center
(x_ctr, y_ctr), output a set of anchors (windows).
"""
ws = ws[:, np.newaxis]
hs = hs[:, np.newaxis]
anchors = np.hstack((x_ctr - 0.5 * (ws - 1),
y_ctr - 0.5 * (hs - 1),
x_ctr + 0.5 * (ws - 1),
y_ctr + 0.5 * (hs - 1)))
return anchors
def _ratio_enum(anchor, ratios):
"""
Enumerate a set of anchors for each aspect ratio wrt an anchor.
"""
w, h, x_ctr, y_ctr = _whctrs(anchor)
size = w * h
size_ratios = size / ratios
ws = np.round(np.sqrt(size_ratios))
hs = np.round(ws * ratios)
anchors = _mkanchors(ws, hs, x_ctr, y_ctr)
return anchors
def _scale_enum(anchor, scales):
"""
Enumerate a set of anchors for each scale wrt an anchor.
"""
w, h, x_ctr, y_ctr = _whctrs(anchor)
ws = w * scales
hs = h * scales
anchors = _mkanchors(ws, hs, x_ctr, y_ctr)
return anchors
if __name__ == '__main__':
import time
t = time.time()
a = generate_anchors()
print(time.time() - t)
print(a)
from IPython import embed;
embed()
| 3,129 | 25.525424 | 78 | py |
RGB-N | RGB-N-master/lib/layer_utils/proposal_target_layer.py | # --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick, Sean Bell and Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import numpy.random as npr
from model.config import cfg
from model.bbox_transform import bbox_transform
from utils.cython_bbox import bbox_overlaps
def proposal_target_layer(rpn_rois, rpn_scores, gt_boxes, _num_classes):
"""
Assign object detection proposals to ground-truth targets. Produces proposal
classification labels and bounding-box regression targets.
"""
# Proposal ROIs (0, x1, y1, x2, y2) coming from RPN
# (i.e., rpn.proposal_layer.ProposalLayer), or any other source
all_rois = rpn_rois
all_scores = rpn_scores
# Include ground-truth boxes in the set of candidate rois
if cfg.TRAIN.USE_GT:
zeros = np.zeros((gt_boxes.shape[0], 1), dtype=gt_boxes.dtype)
all_rois = np.vstack(
(all_rois, np.hstack((zeros, gt_boxes[:, :-1])))
)
# not sure if it a wise appending, but anyway i am not using it
all_scores = np.vstack((all_scores, zeros))
num_images = 1
rois_per_image = cfg.TRAIN.BATCH_SIZE / num_images
fg_rois_per_image = np.round(cfg.TRAIN.FG_FRACTION * rois_per_image)
# Sample rois with classification labels and bounding box regression
# targets
labels, rois, roi_scores, bbox_targets, bbox_inside_weights = _sample_rois(
all_rois, all_scores, gt_boxes, fg_rois_per_image,
rois_per_image, _num_classes)
rois = rois.reshape(-1, 5)
roi_scores = roi_scores.reshape(-1)
labels = labels.reshape(-1, 1)
bbox_targets = bbox_targets.reshape(-1, _num_classes * 4)
bbox_inside_weights = bbox_inside_weights.reshape(-1, _num_classes * 4)
bbox_outside_weights = np.array(bbox_inside_weights > 0).astype(np.float32)
return rois, roi_scores, labels, bbox_targets, bbox_inside_weights, bbox_outside_weights
def _get_bbox_regression_labels(bbox_target_data, num_classes):
"""Bounding-box regression targets (bbox_target_data) are stored in a
compact form N x (class, tx, ty, tw, th)
This function expands those targets into the 4-of-4*K representation used
by the network (i.e. only one class has non-zero targets).
Returns:
bbox_target (ndarray): N x 4K blob of regression targets
bbox_inside_weights (ndarray): N x 4K blob of loss weights
"""
clss = bbox_target_data[:, 0]
bbox_targets = np.zeros((clss.size, 4 * num_classes), dtype=np.float32)
bbox_inside_weights = np.zeros(bbox_targets.shape, dtype=np.float32)
if num_classes<=2:
inds = np.where(clss > 0)[0]
else:
inds = np.where(clss > 0)[0]
for ind in inds:
cls = clss[ind]
start = int(4 * cls)
end = start + 4
bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS
return bbox_targets, bbox_inside_weights
def _compute_targets(ex_rois, gt_rois, labels):
"""Compute bounding-box regression targets for an image."""
assert ex_rois.shape[0] == gt_rois.shape[0]
assert ex_rois.shape[1] == 4
assert gt_rois.shape[1] == 4
targets = bbox_transform(ex_rois, gt_rois)
if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED:
# Optionally normalize targets by a precomputed mean and stdev
targets = ((targets - np.array(cfg.TRAIN.BBOX_NORMALIZE_MEANS))
/ np.array(cfg.TRAIN.BBOX_NORMALIZE_STDS))
return np.hstack(
(labels[:, np.newaxis], targets)).astype(np.float32, copy=False)
def _sample_rois(all_rois, all_scores, gt_boxes, fg_rois_per_image, rois_per_image, num_classes):
"""Generate a random sample of RoIs comprising foreground and background
examples.
"""
# overlaps: (rois x gt_boxes)
overlaps = bbox_overlaps(
np.ascontiguousarray(all_rois[:, 1:5], dtype=np.float),
np.ascontiguousarray(gt_boxes[:, :4], dtype=np.float))
gt_assignment = overlaps.argmax(axis=1)
max_overlaps = overlaps.max(axis=1)
labels = gt_boxes[gt_assignment, 4]
# Select foreground RoIs as those with >= FG_THRESH overlap
fg_inds = np.where(max_overlaps >= cfg.TRAIN.FG_THRESH)[0]
# Guard against the case when an image has fewer than fg_rois_per_image
# Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)
bg_inds = np.where((max_overlaps < cfg.TRAIN.BG_THRESH_HI) &
(max_overlaps >= cfg.TRAIN.BG_THRESH_LO))[0]
# Small modification to the original version where we ensure a fixed number of regions are sampled
if fg_inds.size > 0 and bg_inds.size > 0:
fg_rois_per_image = min(fg_rois_per_image, fg_inds.size)
fg_inds = npr.choice(fg_inds, size=int(fg_rois_per_image), replace=False)
bg_rois_per_image = rois_per_image - fg_rois_per_image
to_replace = bg_inds.size < bg_rois_per_image
bg_inds = npr.choice(bg_inds, size=int(bg_rois_per_image), replace=to_replace)
elif fg_inds.size > 0:
to_replace = fg_inds.size < rois_per_image
fg_inds = npr.choice(fg_inds, size=int(rois_per_image), replace=to_replace)
fg_rois_per_image = rois_per_image
elif bg_inds.size > 0:
to_replace = bg_inds.size < rois_per_image
bg_inds = npr.choice(bg_inds, size=int(rois_per_image), replace=to_replace)
fg_rois_per_image = 0
else:
import pdb
pdb.set_trace()
# The indices that we're selecting (both fg and bg)
keep_inds = np.append(fg_inds, bg_inds)
# Select sampled values from various arrays:
labels = labels[keep_inds]
# Clamp labels for the background RoIs to 0
labels[int(fg_rois_per_image):] = 0
rois = all_rois[keep_inds]
roi_scores = all_scores[keep_inds]
bbox_target_data = _compute_targets(
rois[:, 1:5], gt_boxes[gt_assignment[keep_inds], :4], labels)
bbox_targets, bbox_inside_weights = \
_get_bbox_regression_labels(bbox_target_data, num_classes)
return labels, rois, roi_scores, bbox_targets, bbox_inside_weights
| 6,081 | 37.987179 | 100 | py |
RGB-N | RGB-N-master/lib/layer_utils/snippets.py | # --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import numpy.random as npr
from model.config import cfg
from layer_utils.generate_anchors import generate_anchors
from model.bbox_transform import bbox_transform_inv, clip_boxes
from utils.cython_bbox import bbox_overlaps
def generate_anchors_pre(height, width, feat_stride, anchor_scales=(8,16,32), anchor_ratios=(0.5,1,2)):
""" A wrapper function to generate anchors given different scales
Also return the number of anchors in variable 'length'
"""
anchors = generate_anchors(ratios=np.array(anchor_ratios), scales=np.array(anchor_scales))
A = anchors.shape[0]
shift_x = np.arange(0, width) * feat_stride
shift_y = np.arange(0, height) * feat_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose()
K = shifts.shape[0]
# width changes faster, so here it is H, W, C
anchors = anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2))
anchors = anchors.reshape((K * A, 4)).astype(np.float32, copy=False)
length = np.int32(anchors.shape[0])
return anchors, length
| 1,473 | 42.352941 | 103 | py |
RGB-N | RGB-N-master/lib/layer_utils/anchor_target_layer.py | # --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from model.config import cfg
import numpy as np
import numpy.random as npr
from utils.cython_bbox import bbox_overlaps
from model.bbox_transform import bbox_transform
def anchor_target_layer(rpn_cls_score, gt_boxes, im_info, _feat_stride, all_anchors, num_anchors):
"""Same as the anchor target layer in original Fast/er RCNN """
A = num_anchors
total_anchors = all_anchors.shape[0]
K = total_anchors / num_anchors
im_info = im_info[0]
# allow boxes to sit over the edge by a small amount
_allowed_border = 0
# map of shape (..., H, W)
height, width = rpn_cls_score.shape[1:3]
# only keep anchors inside the image
inds_inside = np.where(
(all_anchors[:, 0] >= -_allowed_border) &
(all_anchors[:, 1] >= -_allowed_border) &
(all_anchors[:, 2] < im_info[1] + _allowed_border) & # width
(all_anchors[:, 3] < im_info[0] + _allowed_border) # height
)[0]
# keep only inside anchors
anchors = all_anchors[inds_inside, :]
# label: 1 is positive, 0 is negative, -1 is dont care
labels = np.empty((len(inds_inside),), dtype=np.float32)
labels.fill(-1)
# overlaps between the anchors and the gt boxes
# overlaps (ex, gt)
overlaps = bbox_overlaps(
np.ascontiguousarray(anchors, dtype=np.float),
np.ascontiguousarray(gt_boxes, dtype=np.float))
argmax_overlaps = overlaps.argmax(axis=1)
max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps]
gt_argmax_overlaps = overlaps.argmax(axis=0)
gt_max_overlaps = overlaps[gt_argmax_overlaps,
np.arange(overlaps.shape[1])]
gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0]
if not cfg.TRAIN.RPN_CLOBBER_POSITIVES:
# assign bg labels first so that positive labels can clobber them
# first set the negatives
labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0
# fg label: for each gt, anchor with highest overlap
labels[gt_argmax_overlaps] = 1
# fg label: above threshold IOU
labels[max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP] = 1
if cfg.TRAIN.RPN_CLOBBER_POSITIVES:
# assign bg labels last so that negative labels can clobber positives
labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0
# subsample positive labels if we have too many
num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE)
fg_inds = np.where(labels == 1)[0]
if len(fg_inds) > num_fg:
disable_inds = npr.choice(
fg_inds, size=(len(fg_inds) - num_fg), replace=False)
labels[disable_inds] = -1
# subsample negative labels if we have too many
num_bg = cfg.TRAIN.RPN_BATCHSIZE - np.sum(labels == 1)
bg_inds = np.where(labels == 0)[0]
if len(bg_inds) > num_bg:
disable_inds = npr.choice(
bg_inds, size=(len(bg_inds) - num_bg), replace=False)
labels[disable_inds] = -1
bbox_targets = np.zeros((len(inds_inside), 4), dtype=np.float32)
bbox_targets = _compute_targets(anchors, gt_boxes[argmax_overlaps, :])
bbox_inside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
# only the positive ones have regression targets
bbox_inside_weights[labels == 1, :] = np.array(cfg.TRAIN.RPN_BBOX_INSIDE_WEIGHTS)
bbox_outside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
if cfg.TRAIN.RPN_POSITIVE_WEIGHT < 0:
# uniform weighting of examples (given non-uniform sampling)
num_examples = np.sum(labels >= 0)
positive_weights = np.ones((1, 4)) * 1.0 / num_examples
negative_weights = np.ones((1, 4)) * 1.0 / num_examples
else:
assert ((cfg.TRAIN.RPN_POSITIVE_WEIGHT > 0) &
(cfg.TRAIN.RPN_POSITIVE_WEIGHT < 1))
positive_weights = (cfg.TRAIN.RPN_POSITIVE_WEIGHT /
np.sum(labels == 1))
negative_weights = ((1.0 - cfg.TRAIN.RPN_POSITIVE_WEIGHT) /
np.sum(labels == 0))
bbox_outside_weights[labels == 1, :] = positive_weights
bbox_outside_weights[labels == 0, :] = negative_weights
# map up to original set of anchors
labels = _unmap(labels, total_anchors, inds_inside, fill=-1)
bbox_targets = _unmap(bbox_targets, total_anchors, inds_inside, fill=0)
bbox_inside_weights = _unmap(bbox_inside_weights, total_anchors, inds_inside, fill=0)
bbox_outside_weights = _unmap(bbox_outside_weights, total_anchors, inds_inside, fill=0)
# labels
labels = labels.reshape((1, height, width, A)).transpose(0, 3, 1, 2)
labels = labels.reshape((1, 1, A * height, width))
rpn_labels = labels
# bbox_targets
bbox_targets = bbox_targets \
.reshape((1, height, width, A * 4))
rpn_bbox_targets = bbox_targets
# bbox_inside_weights
bbox_inside_weights = bbox_inside_weights \
.reshape((1, height, width, A * 4))
rpn_bbox_inside_weights = bbox_inside_weights
# bbox_outside_weights
bbox_outside_weights = bbox_outside_weights \
.reshape((1, height, width, A * 4))
rpn_bbox_outside_weights = bbox_outside_weights
return rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights
def _unmap(data, count, inds, fill=0):
""" Unmap a subset of item (data) back to the original set of items (of
size count) """
if len(data.shape) == 1:
ret = np.empty((count,), dtype=np.float32)
ret.fill(fill)
ret[inds] = data
else:
ret = np.empty((count,) + data.shape[1:], dtype=np.float32)
ret.fill(fill)
ret[inds, :] = data
return ret
def _compute_targets(ex_rois, gt_rois):
"""Compute bounding-box regression targets for an image."""
assert ex_rois.shape[0] == gt_rois.shape[0]
assert ex_rois.shape[1] == 4
assert gt_rois.shape[1] == 5
return bbox_transform(ex_rois, gt_rois[:, :4]).astype(np.float32, copy=False)
| 6,031 | 35.780488 | 98 | py |
RGB-N | RGB-N-master/lib/layer_utils/__init__.py | 0 | 0 | 0 | py |
|
RGB-N | RGB-N-master/lib/utils/nms.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
import numpy as np
def nms(dets, thresh):
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1]
return keep
| 1,008 | 25.552632 | 59 | py |
RGB-N | RGB-N-master/lib/utils/timer.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
import time
class Timer(object):
"""A simple timer."""
def __init__(self):
self.total_time = 0.
self.calls = 0
self.start_time = 0.
self.diff = 0.
self.average_time = 0.
def tic(self):
# using time.time instead of time.clock because time time.clock
# does not normalize for multithreading
self.start_time = time.time()
def toc(self, average=True):
self.diff = time.time() - self.start_time
self.total_time += self.diff
self.calls += 1
self.average_time = self.total_time / self.calls
if average:
return self.average_time
else:
return self.diff
| 948 | 27.757576 | 71 | py |
RGB-N | RGB-N-master/lib/utils/boxes_grid.py | # --------------------------------------------------------
# Subcategory CNN
# Copyright (c) 2015 CVGL Stanford
# Licensed under The MIT License [see LICENSE for details]
# Written by Yu Xiang
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import math
from model.config import cfg
def get_boxes_grid(image_height, image_width):
"""
Return the boxes on image grid.
"""
# height and width of the heatmap
if cfg.NET_NAME == 'CaffeNet':
height = np.floor((image_height * max(cfg.TRAIN.SCALES) - 1) / 4.0 + 1)
height = np.floor((height - 1) / 2.0 + 1 + 0.5)
height = np.floor((height - 1) / 2.0 + 1 + 0.5)
width = np.floor((image_width * max(cfg.TRAIN.SCALES) - 1) / 4.0 + 1)
width = np.floor((width - 1) / 2.0 + 1 + 0.5)
width = np.floor((width - 1) / 2.0 + 1 + 0.5)
elif cfg.NET_NAME == 'VGGnet':
height = np.floor(image_height * max(cfg.TRAIN.SCALES) / 2.0 + 0.5)
height = np.floor(height / 2.0 + 0.5)
height = np.floor(height / 2.0 + 0.5)
height = np.floor(height / 2.0 + 0.5)
width = np.floor(image_width * max(cfg.TRAIN.SCALES) / 2.0 + 0.5)
width = np.floor(width / 2.0 + 0.5)
width = np.floor(width / 2.0 + 0.5)
width = np.floor(width / 2.0 + 0.5)
else:
assert (1), 'The network architecture is not supported in utils.get_boxes_grid!'
# compute the grid box centers
h = np.arange(height)
w = np.arange(width)
y, x = np.meshgrid(h, w, indexing='ij')
centers = np.dstack((x, y))
centers = np.reshape(centers, (-1, 2))
num = centers.shape[0]
# compute width and height of grid box
area = cfg.TRAIN.KERNEL_SIZE * cfg.TRAIN.KERNEL_SIZE
aspect = cfg.TRAIN.ASPECTS # height / width
num_aspect = len(aspect)
widths = np.zeros((1, num_aspect), dtype=np.float32)
heights = np.zeros((1, num_aspect), dtype=np.float32)
for i in range(num_aspect):
widths[0, i] = math.sqrt(area / aspect[i])
heights[0, i] = widths[0, i] * aspect[i]
# construct grid boxes
centers = np.repeat(centers, num_aspect, axis=0)
widths = np.tile(widths, num).transpose()
heights = np.tile(heights, num).transpose()
x1 = np.reshape(centers[:, 0], (-1, 1)) - widths * 0.5
x2 = np.reshape(centers[:, 0], (-1, 1)) + widths * 0.5
y1 = np.reshape(centers[:, 1], (-1, 1)) - heights * 0.5
y2 = np.reshape(centers[:, 1], (-1, 1)) + heights * 0.5
boxes_grid = np.hstack((x1, y1, x2, y2)) / cfg.TRAIN.SPATIAL_SCALE
return boxes_grid, centers[:, 0], centers[:, 1]
| 2,599 | 34.135135 | 84 | py |
RGB-N | RGB-N-master/lib/utils/blob.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Blob helper functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import cv2
def im_list_to_blob(ims):
"""Convert a list of images into a network input.
Assumes images are already prepared (means subtracted, BGR order, ...).
"""
max_shape = np.array([im.shape for im in ims]).max(axis=0)
num_images = len(ims)
blob = np.zeros((num_images, max_shape[0], max_shape[1], 3),
dtype=np.float32)
for i in range(num_images):
im = ims[i]
blob[i, 0:im.shape[0], 0:im.shape[1], :] = im
return blob
def prep_im_for_blob(im, pixel_means, target_size, max_size):
"""Mean subtract and scale an image for use in a blob."""
im = im.astype(np.float32, copy=False)
im -= pixel_means
im_shape = im.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
im_scale = float(target_size) / float(im_size_min)
# Prevent the biggest axis from being more than MAX_SIZE
if np.round(im_scale * im_size_max) > max_size:
im_scale = float(max_size) / float(im_size_max)
im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale,
interpolation=cv2.INTER_LINEAR)
return im, im_scale
def prep_noise_for_blob(im, pixel_means, target_size, max_size):
"""Mean subtract and scale an image for use in a blob."""
im = im.astype(np.float32, copy=False)
im -= pixel_means
im_shape = im.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
im_scale = float(target_size) / float(im_size_min)
# Prevent the biggest axis from being more than MAX_SIZE
if np.round(im_scale * im_size_max) > max_size:
im_scale = float(max_size) / float(im_size_max)
im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale,
interpolation=cv2.INTER_LINEAR)
return im, im_scale
| 2,135 | 32.904762 | 73 | py |
RGB-N | RGB-N-master/lib/utils/__init__.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
| 248 | 34.571429 | 58 | py |
RGB-N | RGB-N-master/lib/model/test.py | # --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import cv2
try:
import cPickle as pickle
except ImportError:
import pickle
import os
import math
from utils.timer import Timer
from utils.cython_nms import nms, nms_new
from utils.boxes_grid import get_boxes_grid
from utils.blob import im_list_to_blob
import pdb
from model.config import cfg, get_output_dir
from model.bbox_transform import clip_boxes, bbox_transform_inv
def _get_image_blob(im):
"""Converts an image into a network input.
Arguments:
im (ndarray): a color image in BGR order
Returns:
blob (ndarray): a data blob holding an image pyramid
im_scale_factors (list): list of image scales (relative to im) used
in the image pyramid
"""
row,col,ch= im.shape
mean = 0
var = 10
sigma = var**0.5
gauss = np.random.normal(mean,sigma,(row,col,ch))
gauss = gauss.reshape(row,col,ch)
# jpeg
#cv2.imwrite('b.jpg',im,[cv2.IMWRITE_JPEG_QUALITY, 70])
#pdb.set_trace()
#im=cv2.imread('b.jpg')
im_orig = im.astype(np.float32, copy=True)
#im_orig = im_orig + gauss
im_orig -= cfg.PIXEL_MEANS
im_shape = im_orig.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
processed_ims = []
processed_noise = []
im_scale_factors = []
for target_size in cfg.TEST.SCALES:
im_scale = float(target_size) / float(im_size_min)
# Prevent the biggest axis from being more than MAX_SIZE
if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale,
interpolation=cv2.INTER_LINEAR)
noise=im
im_scale_factors.append(im_scale)
processed_ims.append(im)
processed_noise.append(noise)
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
noise_blob = im_list_to_blob(processed_noise)
return blob,noise_blob, np.array(im_scale_factors)
def _get_blobs(im):
"""Convert an image and RoIs within that image into network inputs."""
blobs = {}
blobs['data'],blobs['noise'], im_scale_factors = _get_image_blob(im)
return blobs, im_scale_factors
def _clip_boxes(boxes, im_shape):
"""Clip boxes to image boundaries."""
# x1 >= 0
boxes[:, 0::4] = np.maximum(boxes[:, 0::4], 0)
# y1 >= 0
boxes[:, 1::4] = np.maximum(boxes[:, 1::4], 0)
# x2 < im_shape[1]
boxes[:, 2::4] = np.minimum(boxes[:, 2::4], im_shape[1] - 1)
# y2 < im_shape[0]
boxes[:, 3::4] = np.minimum(boxes[:, 3::4], im_shape[0] - 1)
return boxes
def _rescale_boxes(boxes, inds, scales):
"""Rescale boxes according to image rescaling."""
for i in range(boxes.shape[0]):
boxes[i,:] = boxes[i,:] / scales[int(inds[i])]
return boxes
def im_detect(sess, net, im):
blobs, im_scales = _get_blobs(im)
assert len(im_scales) == 1, "Only single-image batch implemented"
im_blob = blobs['data']
# seems to have height, width, and image scales
# still not sure about the scale, maybe full image it is 1.
blobs['im_info'] = np.array([[im_blob.shape[1], im_blob.shape[2], im_scales[0]]], dtype=np.float32)
try:
scores1, scores, bbox_pred, rois,feat,s = net.test_image(sess, blobs['data'], blobs['im_info'])
except:
scores1, scores, bbox_pred, rois,feat,s = net.test_image(sess, blobs['data'],blobs['noise'], blobs['im_info'])
boxes = rois[:, 1:5] / im_scales[0]
# print(scores.shape, bbox_pred.shape, rois.shape, boxes.shape)
scores = np.reshape(scores, [scores.shape[0], -1])
bbox_pred = np.reshape(bbox_pred, [bbox_pred.shape[0], -1])
if cfg.TEST.BBOX_REG:
# Apply bounding-box regression deltas
box_deltas = bbox_pred
pred_boxes = bbox_transform_inv(boxes, box_deltas)
pred_boxes = _clip_boxes(pred_boxes, im.shape)
else:
# Simply repeat the boxes, once for each class
pred_boxes = np.tile(boxes, (1, scores.shape[1]))
return scores, pred_boxes,feat,s
def apply_nms(all_boxes, thresh):
"""Apply non-maximum suppression to all predicted boxes output by the
test_net method.
"""
num_classes = len(all_boxes)
num_images = len(all_boxes[0])
nms_boxes = [[[] for _ in range(num_images)] for _ in range(num_classes)]
for cls_ind in range(num_classes):
for im_ind in range(num_images):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
inds = np.where((x2 > x1) & (y2 > y1) & (scores > cfg.TEST.DET_THRESHOLD))[0]
dets = dets[inds,:]
if dets == []:
continue
keep = nms(dets, thresh)
if len(keep) == 0:
continue
nms_boxes[cls_ind][im_ind] = dets[keep, :].copy()
return nms_boxes
def test_net(sess, net, imdb, weights_filename, max_per_image=100, thresh=0.0):
np.random.seed(cfg.RNG_SEED)
"""Test a Fast R-CNN network on an image database."""
num_images = len(imdb.image_index)
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in range(num_images)]
for _ in range(imdb.num_classes)]
output_dir = get_output_dir(imdb, weights_filename)
if os.path.isfile(os.path.join(output_dir, 'detections.pkl')):
all_boxes=pickle.load(open(os.path.join(output_dir, 'detections.pkl'),'r'))
else:
# timers
_t = {'im_detect' : Timer(), 'misc' : Timer()}
for i in range(num_images):
im = cv2.imread(imdb.image_path_at(i))
_t['im_detect'].tic()
scores, boxes,_ ,_ = im_detect(sess, net, im)
_t['im_detect'].toc()
_t['misc'].tic()
# skip j = 0, because it's the background class
for j in range(1, imdb.num_classes):
inds = np.where(scores[:, j] > thresh)[0]
cls_scores = scores[inds, j]
cls_boxes = boxes[inds, j*4:(j+1)*4]
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
keep = nms(cls_dets, cfg.TEST.NMS)
cls_dets = cls_dets[keep, :]
all_boxes[j][i] = cls_dets
# Limit to max_per_image detections *over all classes*
if max_per_image > 0:
image_scores = np.hstack([all_boxes[j][i][:, -1]
for j in range(1, imdb.num_classes)])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in range(1, imdb.num_classes):
keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0]
all_boxes[j][i] = all_boxes[j][i][keep, :]
_t['misc'].toc()
print('im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \
.format(i + 1, num_images, _t['im_detect'].average_time,
_t['misc'].average_time))
det_file = os.path.join(output_dir, 'detections_{:f}.pkl'.format(10))
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
print('Evaluating detections')
imdb.evaluate_detections(all_boxes, output_dir)
| 7,355 | 32.589041 | 114 | py |
RGB-N | RGB-N-master/lib/model/bbox_transform.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import pdb
from decimal import Decimal
def bbox_transform(ex_rois, gt_rois):
ex_widths = ex_rois[:, 2] - ex_rois[:, 0] + 1.0
ex_heights = ex_rois[:, 3] - ex_rois[:, 1] + 1.0
ex_ctr_x = ex_rois[:, 0] + 0.5 * ex_widths
ex_ctr_y = ex_rois[:, 1] + 0.5 * ex_heights
gt_widths = gt_rois[:, 2] - gt_rois[:, 0] + 1.0
gt_heights = gt_rois[:, 3] - gt_rois[:, 1] + 1.0
gt_ctr_x = gt_rois[:, 0] + 0.5 * gt_widths
gt_ctr_y = gt_rois[:, 1] + 0.5 * gt_heights
targets_dx = (gt_ctr_x - ex_ctr_x) / ex_widths
targets_dy = (gt_ctr_y - ex_ctr_y) / ex_heights
targets_dw = np.log(gt_widths / ex_widths)
targets_dh = np.log(gt_heights / ex_heights)
targets = np.vstack(
(targets_dx, targets_dy, targets_dw, targets_dh)).transpose()
return targets
def bbox_transform_inv(boxes, deltas):
if boxes.shape[0] == 0:
return np.zeros((0, deltas.shape[1]), dtype=deltas.dtype)
boxes = boxes.astype(deltas.dtype, copy=False)
widths = boxes[:, 2] - boxes[:, 0] + 1.0
heights = boxes[:, 3] - boxes[:, 1] + 1.0
ctr_x = boxes[:, 0] + 0.5 * widths
ctr_y = boxes[:, 1] + 0.5 * heights
dx = deltas[:, 0::4]
dy = deltas[:, 1::4]
dw = deltas[:, 2::4]
dh = deltas[:, 3::4]
dh.astype(Decimal)
dw.astype(Decimal)
pred_ctr_x = dx * widths[:, np.newaxis] + ctr_x[:, np.newaxis]
pred_ctr_y = dy * heights[:, np.newaxis] + ctr_y[:, np.newaxis]
pred_w = np.exp(dw) * widths[:, np.newaxis]
pred_h = np.exp(dh) * heights[:, np.newaxis]
pred_boxes = np.zeros(deltas.shape, dtype=deltas.dtype)
# x1
pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w
# y1
pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h
# x2
pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w
# y2
pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h
return pred_boxes
def clip_boxes(boxes, im_shape):
"""
Clip boxes to image boundaries.
"""
# x1 >= 0
boxes[:, 0::4] = np.maximum(np.minimum(boxes[:, 0::4], im_shape[1] - 1), 0)
# y1 >= 0
boxes[:, 1::4] = np.maximum(np.minimum(boxes[:, 1::4], im_shape[0] - 1), 0)
# x2 < im_shape[1]
boxes[:, 2::4] = np.maximum(np.minimum(boxes[:, 2::4], im_shape[1] - 1), 0)
# y2 < im_shape[0]
boxes[:, 3::4] = np.maximum(np.minimum(boxes[:, 3::4], im_shape[0] - 1), 0)
return boxes
| 2,622 | 30.22619 | 77 | py |
RGB-N | RGB-N-master/lib/model/nms_wrapper.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from model.config import cfg
from nms.gpu_nms import gpu_nms
from nms.cpu_nms import cpu_nms
from nms.py_cpu_nms import py_cpu_nms
def nms(dets, thresh, force_cpu=False):
"""Dispatch to either CPU or GPU NMS implementations."""
if dets.shape[0] == 0:
return []
if cfg.USE_GPU_NMS and not force_cpu:
return gpu_nms(dets, thresh, device_id=cfg.GPU_ID)
else:
return cpu_nms(dets, thresh)
| 764 | 30.875 | 58 | py |
RGB-N | RGB-N-master/lib/model/config.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import os.path as osp
import numpy as np
# `pip install easydict` if you don't have it
from easydict import EasyDict as edict
__C = edict()
# Consumers can get config by:
# from fast_rcnn_config import cfg
cfg = __C
#
# Training options
#
__C.TRAIN = edict()
# Initial learning rate
__C.TRAIN.LEARNING_RATE = 0.001
# Momentum
__C.TRAIN.MOMENTUM = 0.9
# Weight decay, for regularization
__C.TRAIN.WEIGHT_DECAY = 0.0005
# Factor for reducing the learning rate
__C.TRAIN.GAMMA = 0.1
# Step size for reducing the learning rate, currently only support one step
__C.TRAIN.STEPSIZE = 30000
# Iteration intervals for showing the loss during training, on command line interface
__C.TRAIN.DISPLAY = 10
# Whether to double the learning rate for bias
__C.TRAIN.DOUBLE_BIAS = True
# Whether to initialize the weights with truncated normal distribution
__C.TRAIN.TRUNCATED = False
# Whether to have weight decay on bias as well
__C.TRAIN.BIAS_DECAY = False
# Whether to add ground truth boxes to the pool when sampling regions
__C.TRAIN.USE_GT = False
# Whether to use aspect-ratio grouping of training images, introduced merely for saving
# GPU memory
__C.TRAIN.ASPECT_GROUPING = False
# The number of snapshots kept, older ones are deleted to save space
__C.TRAIN.SNAPSHOT_KEPT = 10
# The time interval for saving tensorflow summaries
__C.TRAIN.SUMMARY_INTERVAL = 180
# Scale to use during training (can NOT list multiple scales)
# The scale is the pixel size of an image's shortest side
__C.TRAIN.SCALES = (600,)
# Max pixel size of the longest side of a scaled input image
__C.TRAIN.MAX_SIZE = 1000
# Images to use per minibatch
__C.TRAIN.IMS_PER_BATCH = 1
# Minibatch size (number of regions of interest [ROIs])
__C.TRAIN.BATCH_SIZE = 128
# Fraction of minibatch that is labeled foreground (i.e. class > 0)
__C.TRAIN.FG_FRACTION = 0.25
# Overlap threshold for a ROI to be considered foreground (if >= FG_THRESH)
__C.TRAIN.FG_THRESH = 0.5
# Overlap threshold for a ROI to be considered background (class = 0 if
# overlap in [LO, HI))
__C.TRAIN.BG_THRESH_HI = 0.5
__C.TRAIN.BG_THRESH_LO = 0.1
# Use horizontally-flipped images during training?
__C.TRAIN.USE_FLIPPED = True
# Train bounding-box regressors
__C.TRAIN.BBOX_REG = True
# Overlap required between a ROI and ground-truth box in order for that ROI to
# be used as a bounding-box regression training example
__C.TRAIN.BBOX_THRESH = 0.5
# Iterations between snapshots
__C.TRAIN.SNAPSHOT_ITERS = 4000
# solver.prototxt specifies the snapshot path prefix, this adds an optional
# infix to yield the path: <prefix>[_<infix>]_iters_XYZ.caffemodel
__C.TRAIN.SNAPSHOT_PREFIX = 'res101_faster_rcnn'
# __C.TRAIN.SNAPSHOT_INFIX = ''
# Use a prefetch thread in roi_data_layer.layer
# So far I haven't found this useful; likely more engineering work is required
# __C.TRAIN.USE_PREFETCH = False
# Normalize the targets (subtract empirical mean, divide by empirical stddev)
__C.TRAIN.BBOX_NORMALIZE_TARGETS = True
# Deprecated (inside weights)
__C.TRAIN.BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
# Normalize the targets using "precomputed" (or made up) means and stdevs
# (BBOX_NORMALIZE_TARGETS must also be True)
__C.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED = True
__C.TRAIN.BBOX_NORMALIZE_MEANS = (0.0, 0.0, 0.0, 0.0)
__C.TRAIN.BBOX_NORMALIZE_STDS = (0.1, 0.1, 0.2, 0.2)
# Train using these proposals
__C.TRAIN.PROPOSAL_METHOD = 'gt'
# Make minibatches from images that have similar aspect ratios (i.e. both
# tall and thin or both short and wide) in order to avoid wasting computation
# on zero-padding.
# Use RPN to detect objects
__C.TRAIN.HAS_RPN = True
# IOU >= thresh: positive example
__C.TRAIN.RPN_POSITIVE_OVERLAP = 0.7
# IOU < thresh: negative example
__C.TRAIN.RPN_NEGATIVE_OVERLAP = 0.3
# If an anchor statisfied by positive and negative conditions set to negative
__C.TRAIN.RPN_CLOBBER_POSITIVES = False
# Max number of foreground examples
__C.TRAIN.RPN_FG_FRACTION = 0.5
# Total number of examples
__C.TRAIN.RPN_BATCHSIZE = 256
# NMS threshold used on RPN proposals
__C.TRAIN.RPN_NMS_THRESH = 0.7
# Number of top scoring boxes to keep before apply NMS to RPN proposals
__C.TRAIN.RPN_PRE_NMS_TOP_N = 12000
# Number of top scoring boxes to keep after applying NMS to RPN proposals
__C.TRAIN.RPN_POST_NMS_TOP_N = 2000
# Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale)
# __C.TRAIN.RPN_MIN_SIZE = 16
# Deprecated (outside weights)
__C.TRAIN.RPN_BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
# Give the positive RPN examples weight of p * 1 / {num positives}
# and give negatives a weight of (1 - p)
# Set to -1.0 to use uniform example weighting
__C.TRAIN.RPN_POSITIVE_WEIGHT = -1.0
# Whether to use all ground truth bounding boxes for training,
# For COCO, setting USE_ALL_GT to False will exclude boxes that are flagged as ''iscrowd''
__C.TRAIN.USE_ALL_GT = True
__C.TRAIN.FUSE = True
__C.TRAIN.USE_NOISE = False
__C.TRAIN.USE_NOISE_AUG = False
__C.TRAIN.USE_JPG_AUG = False
__C.TRAIN.HNM = False
#
# Testing options
#
__C.TEST = edict()
# Scale to use during testing (can NOT list multiple scales)
# The scale is the pixel size of an image's shortest side
__C.TEST.SCALES = (600,)
# Max pixel size of the longest side of a scaled input image
__C.TEST.MAX_SIZE = 1000
# Overlap threshold used for non-maximum suppression (suppress boxes with
# IoU >= this threshold)
__C.TEST.NMS = 0.2
# Experimental: treat the (K+1) units in the cls_score layer as linear
# predictors (trained, eg, with one-vs-rest SVMs).
__C.TEST.SVM = False
# Test using bounding-box regressors
__C.TEST.BBOX_REG = True
# Propose boxes
__C.TEST.HAS_RPN = False
# Test using these proposals
__C.TEST.PROPOSAL_METHOD = 'gt'
## NMS threshold used on RPN proposals
__C.TEST.RPN_NMS_THRESH = 0.7
## Number of top scoring boxes to keep before apply NMS to RPN proposals
__C.TEST.RPN_PRE_NMS_TOP_N = 6000
## Number of top scoring boxes to keep after applying NMS to RPN proposals
__C.TEST.RPN_POST_NMS_TOP_N = 100
# Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale)
# __C.TEST.RPN_MIN_SIZE = 16
# Testing mode, default to be 'nms', 'top' is slower but better
# See report for details
__C.TEST.MODE = 'nms'
# Only useful when TEST.MODE is 'top', specifies the number of top proposals to select
__C.TEST.RPN_TOP_N = 5000
#
# ResNet options
#
__C.RESNET = edict()
# Option to set if max-pooling is appended after crop_and_resize.
# if true, the region will be resized to a squre of 2xPOOLING_SIZE,
# then 2x2 max-pooling is applied; otherwise the region will be directly
# resized to a square of POOLING_SIZE
__C.RESNET.MAX_POOL = False
# Number of fixed blocks during finetuning, by default the first of all 4 blocks is fixed
# Range: 0 (none) to 3 (all)
__C.RESNET.FIXED_BLOCKS = 1
# Whether to tune the batch nomalization parameters during training
__C.RESNET.BN_TRAIN = False
#
# MISC
#
# The mapping from image coordinates to feature map coordinates might cause
# some boxes that are distinct in image space to become identical in feature
# coordinates. If DEDUP_BOXES > 0, then DEDUP_BOXES is used as the scale factor
# for identifying duplicate boxes.
# 1/16 is correct for {Alex,Caffe}Net, VGG_CNN_M_1024, and VGG16
__C.DEDUP_BOXES = 1. / 16.
# Pixel mean values (BGR order) as a (1, 1, 3) array
# We use the same pixel mean for all networks even though it's not exactly what
# they were trained with
__C.PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]])
# For reproducibility
__C.RNG_SEED = 3
# A small number that's used many times
__C.EPS = 1e-14
# Root directory of project
__C.ROOT_DIR = osp.abspath(osp.join(osp.dirname(__file__), '..', '..'))
# Data directory
__C.DATA_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'data'))
# Name (or path to) the matlab executable
__C.MATLAB = 'matlab'
# Place outputs under an experiments directory
__C.EXP_DIR = 'default'
# Use GPU implementation of non-maximum suppression
__C.USE_GPU_NMS = True
# Default GPU device id
__C.GPU_ID = 0
# Default pooling mode, only 'crop' is available
__C.POOLING_MODE = 'crop'
# Size of the pooled region after RoI pooling
__C.POOLING_SIZE = 7
# Anchor scales for RPN
__C.ANCHOR_SCALES = [8,16,32]
# Anchor ratios for RPN
__C.ANCHOR_RATIOS = [0.5,1,2]
def get_output_dir(imdb, weights_filename):
"""Return the directory where experimental artifacts are placed.
If the directory does not exist, it is created.
A canonical path is built using the name from an imdb and a network
(if not None).
"""
outdir = osp.abspath(osp.join(__C.ROOT_DIR, 'output', __C.EXP_DIR, imdb.name))
if weights_filename is None:
weights_filename = 'default'
outdir = osp.join(outdir, weights_filename)
if not os.path.exists(outdir):
os.makedirs(outdir)
return outdir
def get_output_tb_dir(imdb, weights_filename):
"""Return the directory where tensorflow summaries are placed.
If the directory does not exist, it is created.
A canonical path is built using the name from an imdb and a network
(if not None).
"""
outdir = osp.abspath(osp.join(__C.ROOT_DIR, 'tensorboard', __C.EXP_DIR, imdb.name))
if weights_filename is None:
weights_filename = 'default'
outdir = osp.join(outdir, weights_filename)
if not os.path.exists(outdir):
os.makedirs(outdir)
return outdir
def _merge_a_into_b(a, b):
"""Merge config dictionary a into config dictionary b, clobbering the
options in b whenever they are also specified in a.
"""
if type(a) is not edict:
return
for k, v in a.items():
# a must specify keys that are in b
if k not in b:
raise KeyError('{} is not a valid config key'.format(k))
# the types must match, too
old_type = type(b[k])
if old_type is not type(v):
if isinstance(b[k], np.ndarray):
v = np.array(v, dtype=b[k].dtype)
else:
raise ValueError(('Type mismatch ({} vs. {}) '
'for config key: {}').format(type(b[k]),
type(v), k))
# recursively merge dicts
if type(v) is edict:
try:
_merge_a_into_b(a[k], b[k])
except:
print(('Error under config key: {}'.format(k)))
raise
else:
b[k] = v
def cfg_from_file(filename):
"""Load a config file and merge it into the default options."""
import yaml
with open(filename, 'r') as f:
yaml_cfg = edict(yaml.load(f))
_merge_a_into_b(yaml_cfg, __C)
def cfg_from_list(cfg_list):
"""Set config keys via list (e.g., from command line)."""
from ast import literal_eval
assert len(cfg_list) % 2 == 0
for k, v in zip(cfg_list[0::2], cfg_list[1::2]):
key_list = k.split('.')
d = __C
for subkey in key_list[:-1]:
assert subkey in d
d = d[subkey]
subkey = key_list[-1]
assert subkey in d
try:
value = literal_eval(v)
except:
# handle the case when v is a string literal
value = v
assert type(value) == type(d[subkey]), \
'type {} does not match original type {}'.format(
type(value), type(d[subkey]))
d[subkey] = value
| 11,267 | 29.209115 | 91 | py |
RGB-N | RGB-N-master/lib/model/__init__.py | from . import config
| 21 | 10 | 20 | py |
RGB-N | RGB-N-master/lib/model/train_val.py | # --------------------------------------------------------
# Tensorflow RGB-N
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou , based on code from Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from model.config import cfg
import roi_data_layer.roidb as rdl_roidb
from roi_data_layer.layer import RoIDataLayer
from utils.timer import Timer
try:
import cPickle as pickle
except ImportError:
import pickle
import numpy as np
import os
import sys
import glob
import time
import pdb
import tensorflow as tf
from tensorflow.python import pywrap_tensorflow
class SolverWrapper(object):
"""
A wrapper class for the training process
"""
def __init__(self, sess, network, imdb, roidb, valroidb, output_dir, tbdir, pretrained_model=None):
self.net = network
self.imdb = imdb
self.roidb = roidb
self.valroidb = valroidb
self.output_dir = output_dir
self.tbdir = tbdir
# Simply put '_val' at the end to save the summaries from the validation set
self.tbvaldir = tbdir + '_val'
if not os.path.exists(self.tbvaldir):
os.makedirs(self.tbvaldir)
self.pretrained_model = pretrained_model
def snapshot(self, sess, iter):
net = self.net
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
# Store the model snapshot
filename = cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_{:d}'.format(iter) + '.ckpt'
filename = os.path.join(self.output_dir, filename)
self.saver.save(sess, filename)
print('Wrote snapshot to: {:s}'.format(filename))
# Also store some meta information, random state, etc.
nfilename = cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_{:d}'.format(iter) + '.pkl'
nfilename = os.path.join(self.output_dir, nfilename)
# current state of numpy random
st0 = np.random.get_state()
# current position in the database
cur = self.data_layer._cur
# current shuffled indeces of the database
perm = self.data_layer._perm
# current position in the validation database
cur_val = self.data_layer_val._cur
# current shuffled indeces of the validation database
perm_val = self.data_layer_val._perm
# Dump the meta info
with open(nfilename, 'wb') as fid:
pickle.dump(st0, fid, pickle.HIGHEST_PROTOCOL)
pickle.dump(cur, fid, pickle.HIGHEST_PROTOCOL)
pickle.dump(perm, fid, pickle.HIGHEST_PROTOCOL)
pickle.dump(cur_val, fid, pickle.HIGHEST_PROTOCOL)
pickle.dump(perm_val, fid, pickle.HIGHEST_PROTOCOL)
pickle.dump(iter, fid, pickle.HIGHEST_PROTOCOL)
return filename, nfilename
def get_variables_in_checkpoint_file(self, file_name):
try:
reader = pywrap_tensorflow.NewCheckpointReader(file_name)
var_to_shape_map = reader.get_variable_to_shape_map()
return var_to_shape_map
except Exception as e: # pylint: disable=broad-except
print(str(e))
if "corrupted compressed block contents" in str(e):
print("It's likely that your checkpoint file has been compressed "
"with SNAPPY.")
def train_model(self, sess, max_iters):
# Build data layers for both training and validation set
self.data_layer = RoIDataLayer(self.roidb, self.imdb.num_classes)
self.data_layer_val = RoIDataLayer(self.valroidb, self.imdb.num_classes, random=True)
# Determine different scales for anchors, see paper
with sess.graph.as_default():
# Set the random seed for tensorflow
tf.set_random_seed(cfg.RNG_SEED)
# Build the main computation graph
layers = self.net.create_architecture(sess, 'TRAIN', self.imdb.num_classes, tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
# Define the loss
loss = layers['total_loss']
# Set learning rate and momentum
lr = tf.Variable(cfg.TRAIN.LEARNING_RATE, trainable=False)
momentum = cfg.TRAIN.MOMENTUM
self.optimizer = tf.train.MomentumOptimizer(lr, momentum)
# Compute the gradients wrt the loss
gvs = self.optimizer.compute_gradients(loss)
# Double the gradient of the bias if set
if cfg.TRAIN.DOUBLE_BIAS:
final_gvs = []
with tf.variable_scope('Gradient_Mult') as scope:
for grad, var in gvs:
scale = 1.
if cfg.TRAIN.DOUBLE_BIAS and '/biases:' in var.name:
scale *= 2.
if not np.allclose(scale, 1.0):
grad = tf.multiply(grad, scale)
final_gvs.append((tf.clip_by_value(grad,-5.0,5.0), var))
train_op = self.optimizer.apply_gradients(final_gvs)
else:
train_op = self.optimizer.apply_gradients(gvs)
# We will handle the snapshots ourselves
self.saver = tf.train.Saver(max_to_keep=100000)
# Write the train and validation information to tensorboard
self.writer = tf.summary.FileWriter(self.tbdir, sess.graph)
self.valwriter = tf.summary.FileWriter(self.tbvaldir)
# Find previous snapshots if there is any to restore from
sfiles = os.path.join(self.output_dir, cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_*.ckpt.meta')
sfiles = glob.glob(sfiles)
sfiles.sort(key=os.path.getmtime)
# Get the snapshot name in TensorFlow
redstr = '_iter_{:d}.'.format(cfg.TRAIN.STEPSIZE+1)
sfiles = [ss.replace('.meta', '') for ss in sfiles]
sfiles = [ss for ss in sfiles if redstr not in ss]
nfiles = os.path.join(self.output_dir, cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_*.pkl')
nfiles = glob.glob(nfiles)
nfiles.sort(key=os.path.getmtime)
nfiles = [nn for nn in nfiles if redstr not in nn]
lsf = len(sfiles)
assert len(nfiles) == lsf
np_paths = nfiles
ss_paths = sfiles
if lsf == 0:
# Fresh train directly from ImageNet weights
print('Loading initial model weights from {:s}'.format(self.pretrained_model))
variables = tf.global_variables()
# Initialize all variables first
sess.run(tf.variables_initializer(variables, name='init'))
var_keep_dic = self.get_variables_in_checkpoint_file(self.pretrained_model)
# Get the variables to restore, ignorizing the variables to fix
variables_to_restore = self.net.get_variables_to_restore(variables, var_keep_dic)
restorer = tf.train.Saver(variables_to_restore)
restorer.restore(sess, self.pretrained_model)
print('Loaded.')
if cfg.TRAIN.FUSE:
noise_vars={}
for v in variables:
if v.name.split('/')[0]=='noise' and v.name.split(':')[0].replace('noise','resnet_v1_101',1) in var_keep_dic:
noise_vars[v.name.split(':')[0].replace('noise','resnet_v1_101',1)]=v
noise_restorer = tf.train.Saver(noise_vars)
noise_restorer.restore(sess, self.pretrained_model)
# Need to fix the variables before loading, so that the RGB weights are changed to BGR
# For VGG16 it also changes the convolutional weights fc6 and fc7 to
# fully connected weights
self.net.fix_variables(sess, self.pretrained_model)
print('Fixed.')
sess.run(tf.assign(lr, cfg.TRAIN.LEARNING_RATE))
last_snapshot_iter = 0
else:
# Get the most recent snapshot and restore
ss_paths = [ss_paths[-1]]
np_paths = [np_paths[-1]]
print('Restorining model snapshots from {:s}'.format(sfiles[-1]))
self.saver.restore(sess, str(sfiles[-1]))
print('Restored.')
# Needs to restore the other hyperparameters/states for training, (TODO xinlei) I have
# tried my best to find the random states so that it can be recovered exactly
# However the Tensorflow state is currently not available
with open(str(nfiles[-1]), 'rb') as fid:
st0 = pickle.load(fid)
cur = pickle.load(fid)
perm = pickle.load(fid)
cur_val = pickle.load(fid)
perm_val = pickle.load(fid)
last_snapshot_iter = pickle.load(fid)
np.random.set_state(st0)
self.data_layer._cur = cur
self.data_layer._perm = perm
self.data_layer_val._cur = cur_val
self.data_layer_val._perm = perm_val
# Set the learning rate, only reduce once
if last_snapshot_iter > cfg.TRAIN.STEPSIZE:
sess.run(tf.assign(lr, cfg.TRAIN.LEARNING_RATE * cfg.TRAIN.GAMMA))
else:
sess.run(tf.assign(lr, cfg.TRAIN.LEARNING_RATE))
timer = Timer()
iter = last_snapshot_iter + 1
last_summary_time = time.time()
while iter < max_iters + 1:
# Learning rate
if iter == cfg.TRAIN.STEPSIZE + 1:
# Add snapshot here before reducing the learning rate
self.snapshot(sess, iter)
sess.run(tf.assign(lr, cfg.TRAIN.LEARNING_RATE * cfg.TRAIN.GAMMA))
timer.tic()
# Get training data, one batch at a time
blobs = self.data_layer.forward()
now = time.time()
if now - last_summary_time > cfg.TRAIN.SUMMARY_INTERVAL:
# Compute the graph with summary
rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, total_loss, summary = \
self.net.train_step_with_summary(sess, blobs, train_op)
self.writer.add_summary(summary, float(iter))
# Also check the summary on the validation set
blobs_val = self.data_layer_val.forward()
summary_val = self.net.get_summary(sess, blobs_val)
self.valwriter.add_summary(summary_val, float(iter))
last_summary_time = now
else:
# Compute the graph without summary
rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, total_loss = \
self.net.train_step(sess, blobs, train_op)
timer.toc()
# Display training information
if iter % (cfg.TRAIN.DISPLAY) == 0:
print('iter: %d / %d, total loss: %.6f\n >>> rpn_loss_cls: %.6f\n '
'>>> rpn_loss_box: %.6f\n >>> loss_cls: %.6f\n >>> loss_box: %.6f\n >>> lr: %f' % \
(iter, max_iters, total_loss, rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, lr.eval()))
print('speed: {:.3f}s / iter'.format(timer.average_time))
if iter % cfg.TRAIN.SNAPSHOT_ITERS == 0:
last_snapshot_iter = iter
snapshot_path, np_path = self.snapshot(sess, iter)
np_paths.append(np_path)
ss_paths.append(snapshot_path)
# Remove the old snapshots if there are too many
if len(np_paths) > cfg.TRAIN.SNAPSHOT_KEPT:
to_remove = len(np_paths) - cfg.TRAIN.SNAPSHOT_KEPT
for c in range(to_remove):
nfile = np_paths[0]
os.remove(str(nfile))
np_paths.remove(nfile)
if len(ss_paths) > cfg.TRAIN.SNAPSHOT_KEPT:
to_remove = len(ss_paths) - cfg.TRAIN.SNAPSHOT_KEPT
for c in range(to_remove):
sfile = ss_paths[0]
# To make the code compatible to earlier versions of Tensorflow,
# where the naming tradition for checkpoints are different
if os.path.exists(str(sfile)):
os.remove(str(sfile))
else:
os.remove(str(sfile + '.data-00000-of-00001'))
os.remove(str(sfile + '.index'))
sfile_meta = sfile + '.meta'
os.remove(str(sfile_meta))
ss_paths.remove(sfile)
iter += 1
if last_snapshot_iter != iter - 1:
self.snapshot(sess, iter - 1)
self.writer.close()
self.valwriter.close()
def get_training_roidb(imdb):
"""Returns a roidb (Region of Interest database) for use in training."""
if cfg.TRAIN.USE_FLIPPED:
print('Appending horizontally-flipped training examples...')
imdb.append_flipped_images()
print('done')
if cfg.TRAIN.USE_NOISE_AUG:
print('Appending noise to training examples...')
imdb.append_noise_images()
print('done')
if cfg.TRAIN.USE_JPG_AUG:
print('Appending jpg compression to training examples...')
imdb.append_jpg_images()
print('done')
print('Preparing training data...')
rdl_roidb.prepare_roidb(imdb)
print('done')
return imdb.roidb
def filter_roidb(roidb):
"""Remove roidb entries that have no usable RoIs."""
def is_valid(entry):
# Valid images have:
# (1) At least one foreground RoI OR
# (2) At least one background RoI
overlaps = entry['max_overlaps']
# find boxes with sufficient overlap
fg_inds = np.where(overlaps >= cfg.TRAIN.FG_THRESH)[0]
# Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)
bg_inds = np.where((overlaps < cfg.TRAIN.BG_THRESH_HI) &
(overlaps >= cfg.TRAIN.BG_THRESH_LO))[0]
# image is only valid if such boxes exist
valid = len(fg_inds) > 0 or len(bg_inds) > 0
return valid
num = len(roidb)
filtered_roidb = [entry for entry in roidb if is_valid(entry)]
num_after = len(filtered_roidb)
print('Filtered {} roidb entries: {} -> {}'.format(num - num_after,
num, num_after))
return filtered_roidb
def train_net(network, imdb, roidb, valroidb, output_dir, tb_dir,
pretrained_model=None,
max_iters=40000):
"""Train a Fast R-CNN network."""
roidb = filter_roidb(roidb)
valroidb = filter_roidb(valroidb)
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
with tf.Session(config=tfconfig) as sess:
sw = SolverWrapper(sess, network, imdb, roidb, valroidb, output_dir, tb_dir,
pretrained_model=pretrained_model)
print('Solving...')
sw.train_model(sess, max_iters)
print('done solving')
| 13,708 | 37.835694 | 119 | py |
RGB-N | RGB-N-master/lib/nms/py_cpu_nms.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
import numpy as np
def py_cpu_nms(dets, thresh):
"""Pure Python NMS baseline."""
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1]
return keep
| 1,051 | 25.974359 | 59 | py |
RGB-N | RGB-N-master/lib/nms/__init__.py | 0 | 0 | 0 | py |
|
RGB-N | RGB-N-master/coco_synthetic/split_train_test.py | import networkx as nx
import numpy as np
import os
from glob import glob
import sys
import skimage.io as io
import pdb
def contain_node(Graph_list,node):
for g in Graph_list:
if g.has_node(node):
return True
return False
data_dir='../../dataset/filter_tamper' #FIXME
ext='Tp*'
dataDir='../../dataset' #FIXME
dataType='train2014' #COCO2014 train directory
#cls=['person','tv','airplane','dog','bench','train','kite','bed','refrigerator','bowl']
cls=['person','airplane','dog','train','bed','refrigerator']
filenames=glob(os.path.join(data_dir,ext))
G=nx.Graph()
print(len(filenames))
for file in filenames:
content=os.path.splitext(os.path.basename(file))[0].split("_")
if content[-1] in cls:
target_name=content[1]
source_name=content[2]
G.add_edge(target_name,source_name)
train = sorted(nx.connected_component_subgraphs(G), key=len, reverse=True)[0:950]
test=sorted(nx.connected_component_subgraphs(G), key=len, reverse=True)[950:]
with open('train_filter.txt','w') as f:
for file in filenames:
content=os.path.splitext(os.path.basename(a))[0].split("_")
if content[-1] in cls:
target_name=content[1]
source_name=content[2]
if target_name!= source_name and contain_node(train,target_name) and contain_node(train,source_name):
x1=float(content[3])
y1=float(content[4])
x2=float(content[5])
y2=float(content[6])
source_img=io.imread(os.path.join(dataDir,dataType,'COCO_train2014_{:012d}.jpg'.format(int(source_name))))
target_img=io.imread(os.path.join(dataDir,dataType,'COCO_train2014_{:012d}.jpg'.format(int(target_name))))
s_w,s_h = source_img.shape[:2]
t_w,t_h = target_img.shape[:2]
f.write('%s %.5f %.5f %.5f %.5f\n' % (file,x1*s_h/t_h,y1*s_w/t_w,x2*s_h/t_h,y2*s_w/t_w) )
with open('test_filter.txt','w') as f:
for file in filenames:
content=os.path.splitext(os.path.basename(file))[0].split("_")
if content[-1] in cls:
target_name=content[1]
source_name=content[2]
if target_name!= source_name and contain_node(test,target_name) and contain_node(test,source_name):
x1=float(content[3])
y1=float(content[4])
x2=float(content[5])
y2=float(content[6])
source_img=io.imread(os.path.join(dataDir,dataType,'COCO_train2014_{:012d}.jpg'.format(int(source_name))))
target_img=io.imread(os.path.join(dataDir,dataType,'COCO_train2014_{:012d}.jpg'.format(int(target_name))))
s_w,s_h = source_img.shape[:2]
t_w,t_h = target_img.shape[:2]
f.write('%s %.5f %.5f %.5f %.5f\n' % (file,x1*s_h/t_h,y1*s_w/t_w,x2*s_h/t_h,y2*s_w/t_w))
| 2,551 | 33.486486 | 110 | py |
RGB-N | RGB-N-master/coco_synthetic/demo.py | from pycocotools.coco import COCO
import numpy as np
import cv2
import skimage.io as io
import matplotlib.pyplot as plt
import pylab
import os
from PIL import Image
from PIL import ImageFilter
import argparse
import sys
import pdb
def parse_args():
"""
Parse input arguments
"""
parser = argparse.ArgumentParser(description='input begin and end category')
parser.add_argument('--begin', dest='begin',
help='begin type of cat', default=None, type=int)
parser.add_argument('--end', dest='end',
help='begin type of cat',
default=None, type=int)
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
args = parser.parse_args()
return args
args=parse_args()
print(args.begin)
pylab.rcParams['figure.figsize'] = (10.0, 8.0)
dataDir='..'
dataType='train2014'
annFile='%s/annotations/instances_%s.json'%(dataDir,dataType)
coco=COCO(annFile)
cats = coco.loadCats(coco.getCatIds())
for cat in cats[args.begin:args.end]:
for num in range(2000):
try:
catIds = coco.getCatIds(catNms=[cat['name']]);
imgIds = coco.getImgIds(catIds=catIds );
img = coco.loadImgs(imgIds[np.random.randint(0,len(imgIds))])[0]
#I = io.imread('http://mscoco.org/images/%d'%(img['id']))
#I = io.imread(img['coco_url'])
I=io.imread(os.path.join(dataDir,dataType,'COCO_train2014_{:012d}.jpg'.format(img['id'])))
#plt.imshow(I); plt.axis('off')
annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
anns = coco.loadAnns(annIds)
#coco.showAnns(anns)
bbx=anns[0]['bbox']
mask=np.array(coco.annToMask(anns[0]))
print(np.shape(mask))
print(np.shape(I))
#pdb.set_trace()
I1=I
#row,col=np.where(mask>0)
#print(row)
#print(col)
#I1=I[row,col,0]
#print(np.shape(I1))
I1[:,:,0]=np.array(I[:,:,0] * mask )
I1[:,:,1]=np.array(I[:,:,1] * mask )
I1[:,:,2]=np.array(I[:,:,2] * mask )
#pdb.set_trace()
rand=np.random.randint(100,size=1)[0]
#flag=0
#I1=cv2.GaussianBlur(I1,(5,5),0)
#flag=1
img1 = coco.loadImgs(imgIds[np.random.randint(0,len(imgIds))])[0]
#b1 = io.imread('http://mscoco.org/images/%d'%(img1['id']))
#b1 = io.imread(img1['coco_url'])
b1=io.imread(os.path.join(dataDir,dataType,'COCO_train2014_{:012d}.jpg'.format(img1['id'])))
text_img = Image.new('RGBA', (np.shape(b1)[0],np.shape(b1)[1]), (0, 0, 0, 0))
background=Image.fromarray(b1,'RGB')
foreground=Image.fromarray(I1,'RGB').convert('RGBA')
datas=foreground.getdata()
#pdb.set_trace()
newData = []
for item in datas:
if item[0] == 0 and item[1] == 0 and item[2] == 0:
newData.append((0, 0, 0, 0))
else:
newData.append(item)
foreground.putdata(newData)
foreground=foreground.resize((background.size[0],background.size[1]),Image.ANTIALIAS)
background.paste(foreground,(0,0),mask=foreground.split()[3])
if rand%3<2:
background=background.filter(ImageFilter.GaussianBlur(radius=1.5))
#pdb.set_trace()
if not os.path.isfile('../filter_tamper/Tp_'+str(img['id'])+'_'+str(img1['id'])+'_'+str(bbx[0])+'_'+str(bbx[1])+'_'+str(bbx[0]+bbx[2])+'_'+str(bbx[1]+bbx[3])+'_'+cat['name']+'.png'):
io.imsave('../filter_tamper/Tp_'+str(img['id'])+'_'+str(img1['id'])+'_'+str(bbx[0])+'_'+str(bbx[1])+'_'+str(bbx[0]+bbx[2])+'_'+str(bbx[1]+bbx[3])+'_'+cat['name']+'.png',background)
except Exception as e:
print(e)
print('finished')
#I1=np.array([[I[i,j,:] for j in range(len(I[i,:,0])) if mask[i,j]]for i in range(len(I[:,:,0]))])
| 3,500 | 34.363636 | 185 | py |
SOCC | SOCC-master/scripts/socc_comment_profilling.py | import pandas as pd
import ast
import datetime
import numpy as np
"""
Note: This script file is specific designed for SOCC_DATA/raw/gnm_comment_threads.csv,
which can be find in "https://github.com/sfu-discourse-lab/SOCC"
"""
def posted_comments(df):
""" count the posted comments of each user
Args:
df: pandas dataframe (for gnm_comment_threads.csv only)
Returns:
a dataframe with two columns ['comment_author', 'count']
"""
return df[['comment_author','comment_id']].drop_duplicates().\
groupby(['comment_author']).agg(['count'])
def thread_participated(df):
""" count the number of threads that each user participated in
Args:
df: pandas dataframe (for gnm_comment_threads.csv only)
Return:
a dataframe with two columns ['comment_author', 'count_thread_participated']
"""
df['articleID_threadID'] = pd.Series(df.comment_counter.\
apply(lambda x: "_".join(x.split('_')[1:3])))
return df[['comment_author', 'articleID_threadID']].\
drop_duplicates().groupby(['comment_author']).count()
def threads_initiated(df):
""" count the number of threads that each user initiated
Args:
df: pandas dataframe (for gnm_comment_threads.csv only)
Return:
a dataframe with two columns ['comment_author', 'count_thread_initiated']
"""
df['is_initiated_threads'] = df['comment_counter'].apply(lambda x:len(x.split('_'))==3)
return df[['comment_author', 'is_initiated_threads']].groupby(['comment_author']).sum()
def pos_votes_count(df):
""" count the number of positive votes that each user gained
Args:
df: pandas dataframe (for gnm_comment_threads.csv only)
Return:
a dataframe with two columns ['comment_author', 'pos_votes_count']
"""
return df[['comment_author', 'posVotes']].groupby('comment_author').sum()
def neg_votes_count(df):
""" count the number of positive votes that each user gained
Args:
df: pandas dataframe (for gnm_comment_threads.csv only)
Return:
a dataframe with two columns ['comment_author', 'neg_votes_count']
"""
return df[['comment_author', 'negVotes']].groupby('comment_author').sum()
def _find_all_reactions_types(df):
""" This is the helper function
from all the reactions, find all kind of reactions types
Args:
df_reactions: reaction_list column in
pandas dataframe (for gnm_comment_threads.csv only)
Return:
a dataframe with two columns ['comment_author', 'reaction_types_count']
"""
reaction_is_null = df.reactions.isnull()
reactions = []
for i in range(len(df.reactions)):
if not reaction_is_null[i] and len(df.reactions[i]) > 2:
reaction_list = ast.literal_eval(df.reactions[i])['reaction_list']
for rlist in reaction_list:
reactions.append(rlist['reaction'])
return list(set(reactions))
def _find_all_reactions_count(reaction_list):
""" This is the helper function
for each reaction list, find the reaction count
Args:
df: pandas dataframe (for gnm_comment_threads.csv only)
Return:
a dataframe with two columns ['comment_author', 'reaction_types_count']
"""
reactions_count = {}
for rlist in reaction_list:
if rlist['reaction'] in reactions_count:
reactions_count[rlist['reaction']] += 1
else:
reactions_count[rlist['reaction']] = 1
return reactions_count
def reactions_count(df):
""" count the reactions that each user gained among the reaction lists
Args:
df: pandas dataframe (for gnm_comment_threads.csv only)
Return:
a dataframe with two columns ['comment_author', 'reaction_types_count']
"""
result = pd.DataFrame(df.comment_author.drop_duplicates().reset_index(drop=True))
reaction_types = _find_all_reactions_types(df)
#print(reaction_types)
for i in reaction_types:
df[i] = 0
df['reaction_counts'] = 0
reaction_is_null = df.reactions.isnull()
for i in range(len(df.reactions)):
if not reaction_is_null[i] and df.reactions[i] != '{}':
reactions = ast.literal_eval(df.iloc[i]['reactions'])
df.loc[i, 'reaction_counts'] = int(reactions['reaction_counts'][-1].split()[-1])
reaction_list = _find_all_reactions_count(reactions['reaction_list'])
for k in reaction_list.keys():
#print(k) TEST PURPOSE
df.loc[i, k] = int(reaction_list[k])
for t in reaction_types:
raction_type_sum = df[['comment_author', t]].groupby('comment_author').sum()
result = result.join(raction_type_sum, on = 'comment_author')
raction_count_sum = df[['comment_author', 'reaction_counts']].groupby('comment_author').sum()
result = result.join(raction_count_sum, on = 'comment_author')
return result
def yearly_count(df):
""" for each user, count the number of posted comment every year
Args:
df: pandas dataframe (for gnm_comment_threads.csv only)
Return:
a dataframe with few columns ['comment_author',
'comments_posted_in_years', 'yearly_frequency']
"""
timestamp_df = df[['comment_author','timestamp']].dropna()
timestamp_df['year'] = timestamp_df['timestamp'].dropna().\
apply(lambda x: datetime.datetime.fromtimestamp(int(x)/1000).year)
#timestamp_df['thread_year'] = timestamp_df['threadTimestamp'].dropna().apply(lambda x: datetime.datetime.fromtimestamp(int(x)/1000).year)
counts = timestamp_df[['comment_author', 'year']].dropna()\
.groupby(['comment_author', 'year']).size()
counts = pd.DataFrame(counts, columns = ['count'])
yearly_counts = pd.pivot_table(counts, values='count', \
index=['comment_author'], columns=['year'], aggfunc=np.sum)
yearly_counts.columns = ['comments_posted_in_' + str(n) for n in yearly_counts.columns]
yearly_counts['yearly_frequency'] = yearly_counts.sum(axis=1)/len(yearly_counts.columns)
return yearly_counts
def main(path):
df = pd.read_csv('Data/raw/gnm_comment_threads.csv').drop_duplicates()
result = pd.DataFrame(df.comment_author.drop_duplicates().reset_index(drop=True))
result = result.join(posted_comments(df), on = 'comment_author')
result = result.join(thread_participated(df), on = 'comment_author')
result = result.join(threads_initiated(df), on = 'comment_author')
result = result.join(pos_votes_count(df), on = 'comment_author')
result = result.join(neg_votes_count(df), on = 'comment_author')
result = result.join(reactions_count(df).set_index('comment_author'), on = 'comment_author')
result = result.join(yearly_count(df), on = 'comment_author')
result.columns = ['comment_author','posted_comments_count', \
'participated_threads_count', 'initiated_thread_count', \
'posVotes_num', 'negVotes_num'] + list(result.columns[6:])
result.to_csv('commenter_profiles.csv', index=False)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description='count the comment profilling')
parser.add_argument('gnm_comment_threads_file_path', type=str,
help='the path to ICE folder')
args = parser.parse_args()
main(args.gnm_comment_threads_file_path)
| 6,776 | 35.435484 | 139 | py |
SOCC | SOCC-master/scripts/clean_comments.py | import glob
import re
from smart_open import smart_open
# set the directory of your exported project
webanno_project = input("Path to exported WebAnno project: (e.g. 'C:/.../curation')")
write_directory = input("Path to folder to write new TSVs to: (e.g. 'C:/.../clean_TSVs')")
# note that if the folder you write to does not already exist, this may cause an error
def getcontents(directory):
"""
Returns the file paths for all files in the specified path. Basically the same as glob.glob, but adds a slash to
the path and changes backslashes to forward slashes.
:param directory: the path to a folder (without a '/' at the end)
:return: a list of the contents of that folder
"""
return [name.replace('\\', '/') for name in glob.glob(directory + '/*')]
# find the folders in your project
folders = getcontents(webanno_project)
# get the paths to each file in the project
files1 = [getcontents(doc) for doc in folders]
files = []
for i in range(len(files1)):
try:
files.append(files1[i][0])
except:
continue
# clean the files so they can be read as tsv's
# generate new names for cleaned files
commonsource = webanno_project + '/'
commonname = '/CURATION_USER.tsv'
cleannames = [name[-(len(name) - len(commonsource)):] for name in files] # get folder/CURATION_USER.tsv
cleannames = [name[:len(name) - len(commonname)] for name in cleannames] # cut out the /CURATION_USER.tsv part
cleannames = [re.sub(r"\..*", "", name) for name in cleannames] # strip the file extension
cleannames = [name + '_cleaned.tsv' for name in cleannames]
# generate new directories for cleaned files
cleandirs = [write_directory.replace('\\', '/') + '/' + name for name in cleannames]
# actually clean those comments
def cleancomment(path):
"""
Cleans a file of any lines beginning with '#' - these lines prevent the file from being read properly into a Pandas
dataframe.
:param path: the path to a file
:return: the contents of the file, with any lines starting with '#' removed
"""
newfile = []
with smart_open(path, 'r') as f:
for line in f.readlines():
if re.match('#', line) is None:
newfile.append(line)
newfile2 = ''
for line in newfile:
for char in line:
newfile2 = newfile2 + char
return newfile2
def cleancomments(readdirs, writedirs, readnames=[]):
"""
Cleans the comments in readdirs and writes them to writedirs. Be sure the two lists are the same length and order,
or it will return an error.
:param readdirs: a list of files to clean
:param writedirs: a list of files to write to; i.e. paths to the new, clean files
:param readnames: a list of names used to report which file has been cleaned. If unspecified, will not report that
any files have been cleaned (but will still clean them)
:return:
"""
for i in range(max(len(readdirs), len(writedirs))):
with smart_open(writedirs[i], 'w') as f:
f.write(cleancomment(readdirs[i]))
if readnames:
print(readnames[i] + ' cleaned')
# Write cleaned comments to assigned folder
cleancomments(files, cleandirs, readnames=cleannames)
| 3,244 | 35.055556 | 119 | py |
SOCC | SOCC-master/scripts/rename_webanno.py | import glob
import os
import pandas as pd
# This script can be used to rename the Appraisal or Negation annotated files from their idiosyncratic names to those
# generated by the comment counter. You will need the unzipped, exported project directory, as well as a mapping of
# WebAnno to comment counter names. (The mapping and zipped project are available on GitHub.)
# path to your unzipped exported project:
maindir = input("path to your unzipped exported project e.g. 'C:/.../project_name'")
# path to your mapping of names:
mapping_csv = input("path to your mapping of names e.g. 'C:/.../comment_counter_appraisal_mapping.csv'")
# get the subfolders where annotations are
annotations1dir = maindir + '/annotation'
annotations2dir = maindir + '/annotation_ser'
curations1dir = maindir + '/curation'
curations2dir = maindir + '/curation_ser'
sourcedir = maindir + '/source'
# then the files for each annotation
def getcontents(directory):
"""
Returns the file paths for all files in the specified path (directory).
"""
return [name.replace('\\', '/') for name in glob.glob(directory + '/*')]
annotations1 = getcontents(annotations1dir)
annotations2 = getcontents(annotations2dir)
curations1 = getcontents(curations1dir)
curations2 = getcontents(curations2dir)
sources = getcontents(sourcedir)
# make sure all files end in .txt (some may have been .tsv)
def ziplist(oldlist, newlist):
"""
:param oldlist: an iterable
:param newlist: another iterable
:return: list from two iterables oldlist and newlist, where the ith element of oldlist is the first element of the
ith sub-list and the ith element of newlist is the second element of the ith sub-list.
"""
return [[oldlist[i], newlist[i]]
for i in range(max(len(oldlist), len(newlist)))]
def cleanfilenames(files, directory):
"""
:param files: a list of paths to files
:param directory: the directory common to those files (used to rename them)
:return: a ziplist where each sublist's first element is the original filename and the second element is that
name with a .txt extension instead.
This function will not work if file extensions are more than 3 characters.
"""
sourcenames = [name[(len(directory) + 1):] for name in files]
cleannames = [name[(len(directory) + 1):-3] + 'txt' for name in files]
return ziplist(sourcenames, cleannames)
cleanann1 = cleanfilenames(annotations1, annotations1dir)
cleanann2 = cleanfilenames(annotations2, annotations2dir)
cleancur1 = cleanfilenames(curations1, curations1dir)
cleancur2 = cleanfilenames(curations2, curations2dir)
cleansources = cleanfilenames(sources, sourcedir)
# prepare to rename those files so they end in .txt
def rename_file(directory, pattern, titlepattern):
for pathAndFilename in glob.iglob(os.path.join(directory, pattern)):
os.rename(pathAndFilename, os.path.join(directory, titlepattern))
def massrename(directory, dictionary, confirmation='Done!', check=0):
for i in range(len(dictionary)):
if check == 1:
print([dictionary[i][0], dictionary[i][1]])
rename_file(directory, dictionary[i][0], dictionary[i][1])
print(confirmation)
# execute massrename to change file extensions
massrename(annotations1dir, cleanann1, confirmation='ann1')
massrename(annotations2dir, cleanann2, confirmation='ann2')
massrename(curations1dir, cleancur1, confirmation='cur1')
massrename(curations2dir, cleancur2, confirmation='cur2')
massrename(sourcedir, cleansources, confirmation='source')
# get mapping mappings into Python from csv
mapping1 = pd.read_csv(mapping_csv)
list1 = mapping1['appraisal_negation_annotation_file_name'].tolist()
list2 = mapping1['comment_counter'].tolist()
mapping = ziplist(list1, list2)
# rename files according to mapping (e.g. source_....)
massrename(annotations1dir, mapping, confirmation='ann1')
massrename(annotations2dir, mapping, confirmation='ann2')
massrename(curations1dir, mapping, confirmation='cur1')
massrename(curations2dir, mapping, confirmation='cur2')
massrename(sourcedir, mapping, confirmation='source')
| 4,125 | 38.673077 | 118 | py |
SOCC | SOCC-master/scripts/webanno_to_sentence.py | from smart_open import smart_open
import pandas as pd
import re
from io import StringIO
# find the comments
appraisal_comments_path = input('Path to combined Appraisal WebAnno formatted comments tsv'
'(e.g. C:\\...\\combined_appraisal_webanno.tsv): ')
negation_comments_path = input('Path to combined negation WebAnno formatted comments tsv'
'(e.g. C:\\...\\combined_negation_webanno.tsv): ')
mapping_csv = input("Path to your mapping of names e.g. 'C:\\...\\comment_counter_appraisal_mapping.csv'")
contox_path = input("Path to constructiveness and toxicity annotations e.g."
"'C:\\...\\SFU_constructiveness_toxicity_corpus.csv'")
writename = input('Name for the file that will be created (e.g. all_socc_annotations): ') + '.csv'
writepath = input('Folder to write the new file to (e.g. C:\\...\\Documents\\): ') + writename
# what to put for blank entries:
blank_entry = 'None'
# split the comments so that we can iterate over each individual one
if appraisal_comments_path:
with smart_open(appraisal_comments_path, 'r') as f:
comments_str = f.read()
appraisal_comments_list = comments_str.split('#end of comment\n\n')
else:
print("Not using Appraisal annotations as no path was provided.")
if negation_comments_path:
with smart_open(negation_comments_path, 'r') as f:
comments_str = f.read()
negation_comments_list = comments_str.split('#end of comment\n\n')
else:
print("Not using negation annotations as no path was provided.")
# go through the comments, extract the sentences, clean the comments so pandas can read them later, then build a
# pandas data frame combining each comment as it is done
# these are the actual column headers for the TSV files
# some Appraisal TSVs do not have graduation, hence the need for two lists of names
appraisal_longheaders = ['sentpos', 'charpos', 'word', 'attlab', 'attpol', 'gralab', 'grapol']
appraisal_shortheaders = ['sentpos', 'charpos', 'word', 'attlab', 'attpol']
negation_headers = ['sentpos', 'charpos', 'word', 'negation']
# some comments have no annotations
no_annotation = ['sentpos', 'charpos', 'word']
appraisal_possnames = [no_annotation, appraisal_shortheaders, appraisal_longheaders]
negation_possnames = [no_annotation, negation_headers]
# labels that can be found in different columns
attlabs = ('Appreciation', 'Affect', 'Judgment')
attpols = ('pos', 'neu', 'neg')
gralabs = ('Force', 'Focus')
grapols = ('up', 'down')
neglabs = ('NEG', 'SCOPE', 'FOCUS', 'XSCOPE')
# create some tuples to show which columns go with which labels
# doesn't include polarity because we'll pull that out based on the label
# (each instance of Attitude should have both label and polarity)
appraisal_collabels = ((appraisal_longheaders[3], attlabs),
(appraisal_longheaders[5], gralabs),)
# this next tuple is within another tuple so that the same commands we need later will iterate correctly
negation_collabels = (('negation', neglabs),)
# use the mapping csv to provide comment counter names in addition to old names
if mapping_csv:
mapping1 = pd.read_csv(mapping_csv)
list1 = mapping1['appraisal_negation_annotation_file_name'].tolist()
list2 = mapping1['comment_counter'].tolist()
# dictionary of original to comment counter names
mappingdict1 = {}
for i in range(max(len(list1), len(list2))):
mappingdict1[list1[i]] = list2[i]
# same dictionary in reverse
mappingdict2 = {}
for i in range(max(len(list1), len(list2))):
mappingdict2[list2[i]] = list1[i]
def readprojfile(source, project):
"""
Reads a WebAnno TSV into a pandas dataframe. One column is often read as full of NaN's due to the TSVs'
original formatting, so this function drops any columns with NaN's.
:param source: the path to a WebAnno TSV
:param possnames: the headers that may occur in the TSV, as a list of lists of headers.
The function will check each list within possnames to see if its length is equal to the number of columns
:param project: 'app' if Appraisal, 'neg' if negation.
:return: a pandas dataframe containing the information in the original TSV
"""
# set possnames
if project == "neg" or project.lower() == "negation":
possnames = negation_possnames
project = "neg"
elif project == "app" or project.lower() == "appraisal":
possnames = appraisal_possnames
project = "app"
else:
print("Project type not recognized. Use 'neg' or 'att'.")
possnames = None
newdf = pd.read_csv(source, sep='\t', header=None)
newdf = newdf.dropna(axis=1, how='all')
if (project == "neg" or project.lower() == "negation") \
and len(newdf.columns) == 5: # Neg annotations with arrows have an extra column we won't use
newdf = newdf.loc[:, 0:3] # so we'll just delete it
for headers in possnames:
if len(newdf.columns) == len(headers):
newdf.columns = headers
if all([len(newdf.columns) != i for i in [len(headers) for headers in possnames]]):
print("No correct number of columns in", source)
return newdf
def getlabinds_df(dataframe, correspondences, dfname="dataframe", verbose=False):
"""
Gets the unique labels, including indices, that appear in a dataframe so that they can be searched later.
:param dataframe: a pandas dataframe
:param correspondences: a list or tuple of columns and labels like collabels
:param dfname: a name for the dataframe, used for reporting when one or more columns doesn't show up
:param verbose: a boolean; if True, tells you when a dataframe is missing a column
:return: a list of the form [(index of column),(list of unique labels including index of that label, e.g.
['Appreciation','Appreciation[1]','Appreciation[2]'])
"""
newdict = {}
for entry in range(len(correspondences)):
if correspondences[entry][0] in dataframe.columns:
searchedlist = dataframe[correspondences[entry][0]].tolist()
splitlist = [i.split('|') for i in searchedlist]
foundlist = []
for e in splitlist: # each element in splitlist is currently a list
for i in e: # so i is a string
foundlist.append(i) # so now foundlist is a list of strings
foundlist = set(foundlist) # convert to set so we have uniques only
foundlist = [label for label in foundlist] # convert foundlist back to a list
newdict[correspondences[entry][0]] = foundlist
else:
if verbose:
print(dfname, "does not include column", correspondences[entry][0])
return newdict
def lookup_label(dataframe, column, label, commentid="dataframe", not_applicable=None, verbose=False):
"""
Looks in the dataframe for rows matching the label and returns them.
:param dataframe: A pandas dataframe
:param column: which column in the dataframe to look in for the labels
:param label: which label to look for in the column
:param commentid: the name of the comment; the new row will have this as its first entry
:param bothids: whether to include both comment names (e.g. aboriginal_1 and source_xx_xx)
:param not_applicable: what to put in a cell if there is no data (e.g. something un-annotated)
:param verbose: whether to tell you when it's done
:param clean_suffix: the suffix appended to clean files. Default assumes you cleaned them with clean_comments.py
:return: a list that can be used as a new row or rows. If the label has no index (e.g. 'Appreciation' or '_'), then
all rows with those labels will be returned. If it has an index (e.g. 'Appreciation[3]'), then one row
representing that annotated span will be returned.
The fields in the list are, by column:
- the comment ID
- which sentence the span starts in
- which sentence it ends in
- which character it starts on
- which character it ends on
- which words are in the span
- the Attitude label for the span
- the Attitude polarity for the span
- the graduation label for the span
- the graduation polarity for the span
"""
# determine if we're looking at attitude, graduation, or negation
if 'att' in column:
layer = 'att'
elif 'gra' in column:
layer = 'gra'
elif column == 'negation':
layer = 'neg'
else:
layer = 'unknown'
# Check that both label and polarity columns are present
if ('attlab' in dataframe.columns) ^ ('attpol' in dataframe.columns):
if 'attlab' in dataframe.columns:
print(commentid, 'has attlab column but no attpol column')
if 'attpol' in dataframe.columns:
print(commentid, 'has attpol column but no attlab column')
if ('gralab' in dataframe.columns) ^ ('grapol' in dataframe.columns):
if 'gralab' in dataframe.columns:
print(commentid, 'has gralab column but no grapol column')
if 'grapol' in dataframe.columns:
print(commentid, 'has grapol column but no gralab column')
# look for labels with brackets (e.g. 'Appreciation[3]')
if '[' in label:
mask = [(label in i) for i in dataframe[column].tolist()]
founddf = dataframe[mask]
# get the sentence(s) of the label
foundsentstart = int(re.search(r'^.*-', founddf['sentpos'].tolist()[0]).group()[:-1])
foundsentend = int(re.search(r'^.*-', founddf['sentpos'].tolist()[-1]).group()[:-1])
# get the character positions for the new row
# look at which character the label starts in
foundcharstart = int(re.search(r'^.*-', founddf['charpos'].tolist()[0]).group()[:-1])
# look at which character the label ends in
foundcharend = int(re.search(r'-.*$', founddf['charpos'].tolist()[-1]).group()[1:])
# concatenate the words for the new row
foundwords = ''
for word in founddf['word']:
foundwords = foundwords + word + ' '
foundwords = foundwords[:-1]
# get the labels for the new row
# in case of pipes, figure out which one is the real label
posslabels = founddf[column].tolist()
posslabels = posslabels[0]
posslabels = posslabels.split('|')
labelindex = posslabels.index(label)
# now look through the columns and find the appropriate labels
# Each column is converted to a list. The first item in the list is used to find the label.
# This item is split by '|' in case of stacked annotations.
# Before, we found the index of the label we want. We get the found label from this index.
if layer == 'att':
if 'attlab' in founddf.columns:
foundattlab = founddf['attlab'].tolist()[0].split('|')[labelindex]
# We want to cut off the index (e.g. 'Appreciation[3]' -> 'Appreciation')
# search() finds everything up to the '[', and .group()[:-1] returns what it found, minus the '['
foundattlab = re.search(r'^.*\[', foundattlab).group()[:-1]
else:
foundattlab = not_applicable
if 'attpol' in founddf.columns:
foundattpol = founddf['attpol'].tolist()[0].split('|')[labelindex]
foundattpol = re.search(r'^.*\[', foundattpol).group()[:-1]
else:
foundattpol = not_applicable
foundgralab = not_applicable
foundgrapol = not_applicable
elif layer == 'gra':
if 'gralab' in founddf.columns:
foundgralab = founddf['gralab'].tolist()[0].split('|')[labelindex]
foundgralab = re.search(r'^.*\[', foundgralab).group()[:-1]
else:
foundgralab = not_applicable
if 'grapol' in founddf.columns:
foundgrapol = founddf['grapol'].tolist()[0].split('|')[labelindex]
foundgrapol = re.search(r'^.*\[', foundgrapol).group()[:-1]
else:
foundgrapol = not_applicable
foundattlab = not_applicable
foundattpol = not_applicable
elif layer == 'neg':
if 'negation' in founddf.columns:
foundneglab = founddf['negation'].tolist()[0].split('|')[labelindex]
foundneglab = re.search(r'^.*\[', foundneglab).group()[:-1]
else:
foundneglab = not_applicable
else:
print(label, "I can't tell which label this is.")
# put all that together into a list for a new row
if layer == 'att' or layer == 'gra':
foundrow = [commentid, foundsentstart, foundsentend, foundcharstart, foundcharend,
foundwords, foundattlab, foundattpol, foundgralab, foundgrapol]
elif layer == 'neg':
foundrow = [commentid, foundsentstart, foundsentend, foundcharstart, foundcharend,
foundwords, foundneglab]
else:
print("I couldn't make a new row because I don't know which label this is")
if verbose:
print('Done with comment', commentid, "label", label)
return foundrow
# look for unlabelled spans (i.e. label '_')
elif label == '_':
if layer == 'neg':
mask = [(label in i) for i in dataframe[column].tolist()]
founddf = dataframe[mask]
# If the layer is Attitude or Graduation, check for spans with a label but no polarity or vice versa
# and be sure that any spans returned as unlabelled have no label or polarity
elif layer == 'att' or layer == 'gra':
attmask = []
gramask = []
if 'attlab' in dataframe.columns and 'attpol' in dataframe.columns:
mask1 = [(label in i) for i in dataframe['attlab'].tolist()]
mask2 = [(label in i) for i in dataframe['attpol'].tolist()]
for i in range(len(mask1)):
if mask1[i] is not mask2[i]:
print('row', i, 'has mismatched Attitude labels')
attmask = [a and b for a, b in zip(mask1, mask2)]
if 'gralab' in dataframe.columns and 'grapol' in dataframe.columns:
mask3 = [(label in i) for i in dataframe['gralab'].tolist()]
mask4 = [(label in i) for i in dataframe['grapol'].tolist()]
for i in range(len(mask3)):
if mask3[i] is not mask4[i]:
print('row', i, 'has mismatched Graduation labels')
gramask = [a and b for a, b in zip(mask3, mask4)]
if attmask and not gramask:
mask = attmask
elif gramask and not attmask:
mask = gramask
elif attmask and gramask:
mask = [a and b for a, b in zip(attmask, gramask)]
elif not attmask and not gramask: # this will return all rows if there's no attlab or
mask = [True for i in range(len(dataframe))] # gralab, since there's no annotations at all.
founddf = dataframe[mask]
else:
print("Layer unrecognized when looking for unlabelled spans")
# find the sentences
sentences = []
for i in range(len(founddf['sentpos'])):
sentences.append(
int( # we want to do math on this later
re.search(
r'^.*-', founddf['sentpos'].tolist()[i] # finds whatever comes before a '-'
).group()[:-1] # returns the string it found
))
# find the character positions
charpositions = []
for i in range(len(founddf['charpos'])):
charpositions.append(
(int(re.search(r'^.*-', founddf['charpos'].tolist()[i]).group()[:-1]),
int(re.search(r'-.*$', founddf['charpos'].tolist()[i]).group()[1:]))
)
# find all the words
allfoundwords = founddf['word'].tolist()
# find consecutive unlabelled words
foundspans = []
span_number = -1
last_match = False
for i in range(len(allfoundwords)):
if i - 1 in range(len(allfoundwords)): # if this isn't the first word
# check if this word came right after the last one
if sentences[i - 1] == sentences[i] and \
(charpositions[i - 1][-1] == (charpositions[i][0] - 1) or \
charpositions[i - 1][-1] == (charpositions[i][0])):
if not last_match: # if this is not a continuation of the previous span
span_number += 1 # keep track of the number we're on (index of foundspans)
# add the row for this span to foundspans
if layer == 'att' or layer == 'gra':
foundspans.append([commentid, # comment ID
sentences[i - 1], # sentence start
sentences[i], # sentence end
charpositions[i - 1][0], # character start
charpositions[i][-1], # character end
allfoundwords[i - 1] + ' ' + allfoundwords[i], # words
not_applicable, # Labels are all assumed to be absent.
not_applicable, # Per earlier code, it should tell you if that is
not_applicable, # not actually the case.
not_applicable, ])
elif layer == 'neg':
foundspans.append([commentid, # comment ID
sentences[i - 1], # sentence start
sentences[i], # sentence end
charpositions[i - 1][0], # character start
charpositions[i][-1], # character end
allfoundwords[i - 1] + ' ' + allfoundwords[i], # words
not_applicable])
last_match = True # record these two i's as contiguous
else: # (this word is a continuation of the previous span)
foundspans[span_number].pop(4) # remove the ending char position so we can replace it
oldwords = foundspans[span_number].pop(4) # remove the words from the span to replace it
foundspans[span_number].insert(4, charpositions[i][-1]) # add the last character of this word
foundspans[span_number].insert(5, oldwords + ' ' + allfoundwords[i]) # add the words together
else:
last_match = False # record these two i's as non-contiguous
# check if this is the first pair of words we're looking at
if i == 1: # i would equal 1 bc we skip i=0 (since we looked backwards)
# if i=1 and the first and second words are non-contiguous, we need to add
# the first word to foundspans.
if layer == 'att' or layer == 'gra':
foundspans.append([commentid, # comment ID
sentences[i - 1], # sentence start
sentences[i - 1], # sentence end
charpositions[i - 1][0], # character start
charpositions[i - 1][-1], # character end
allfoundwords[i - 1], # word
not_applicable, # Labels are all assumed to be absent.
not_applicable, # Per earlier code, it should tell you if that is
not_applicable, # not actually the case.
not_applicable, ])
elif layer == 'neg':
foundspans.append([commentid, # comment ID
sentences[i - 1], # sentence start
sentences[i - 1], # sentence end
charpositions[i - 1][0], # character start
charpositions[i - 1][-1], # character end
allfoundwords[i - 1], # word
not_applicable])
# look ahead to see if the next word is a continuation of this span:
if i + 1 in range(len(sentences)):
if sentences[i + 1] != sentences[i] and charpositions[i + 1][-1] != (charpositions[i][0] + 1):
span_number = span_number + 1 # if so, keep track of the index
if layer == 'att' or layer == 'gra':
foundspans.append([commentid, # comment ID
sentences[i], # sentence start
sentences[i], # sentence end
charpositions[i][0], # character start
charpositions[i][-1], # character end
allfoundwords[i], # word
not_applicable, # Labels are all assumed to be absent.
not_applicable, # Per earlier code, it should tell you if that is
not_applicable, # not actually the case.
not_applicable, ])
elif layer == 'neg':
foundspans.append([commentid, # comment ID
sentences[i], # sentence start
sentences[i], # sentence end
charpositions[i][0], # character start
charpositions[i][-1], # character end
allfoundwords[i], # word
not_applicable])
# else: the loop continues
else: # if there is no following word and this one isn't a continuation, it's its own word.
span_number = span_number + 1
if layer == 'att' or layer == 'gra':
foundspans.append([commentid, # comment ID
sentences[i], # sentence start
sentences[i], # sentence end
charpositions[i][0], # character start
charpositions[i][-1], # character end
allfoundwords[i], # word
not_applicable, # Labels are all assumed to be absent.
not_applicable, # Per earlier code, it should tell you if that is
not_applicable, # not actually the case.
not_applicable, ])
elif layer == 'neg':
foundspans.append([commentid, # comment ID
sentences[i], # sentence start
sentences[i], # sentence end
charpositions[i][0], # character start
charpositions[i][-1], # character end
allfoundwords[i], # word
not_applicable]) # no negation
if verbose:
print('Done with comment', commentid, "label", label)
return foundspans
# look for one-word annotated spans (e.g. 'Appreciation'
elif ((label in attlabs) or
(label in attpols) or
(label in gralabs) or
(label in grapols) or
(label in neglabs)):
# create subset dataframe - stricter than other conditions
mask = [(label == i) for i in dataframe[column].tolist()]
founddf = dataframe[mask]
# find the sentences
sentences = []
for i in range(len(founddf['sentpos'])):
sentences.append(
int( # we want to do math on this later
re.search(
r'^.*-', founddf['sentpos'].tolist()[i] # finds whatever comes before a '-'
).group()[:-1] # returns the string it found, minus 1 character from the end
))
# find the character positions
charpositions = []
for i in range(len(founddf['charpos'])):
charpositions.append(
(int(re.search(r'^.*-', founddf['charpos'].tolist()[i]).group()[:-1]),
int(re.search(r'-.*$', founddf['charpos'].tolist()[i]).group()[1:]))
)
# find the words
allfoundwords = founddf['word'].tolist()
# in case of pipes, figure out which one is the real label
posslabels = founddf[column].tolist()
posslabels = posslabels[0]
posslabels = posslabels.split('|')
labelindex = posslabels.index(label)
# now look through the columns and find the appropriate labels
# Each column is converted to a list. The first item in the list is used to find the label.
# This item is split by '|' in case of stacked annotations.
# Before, we found the index of the label we want. We get the found label from this index.
foundspans = []
for i in range(len(founddf)):
# since these are one word long, the starting and ending sentences are the same.
foundsentstart = sentences[i]
foundsentend = foundsentstart
# find the characters the word starts and ends with
foundcharstart = charpositions[i][0]
foundcharend = charpositions[i][1]
# find the word
foundwords = allfoundwords[i]
if layer == 'att':
if 'attlab' in founddf.columns:
foundattlab = founddf['attlab'].tolist()[0].split('|')[labelindex]
else:
foundattlab = not_applicable
if 'attpol' in founddf.columns:
foundattpol = founddf['attpol'].tolist()[0].split('|')[labelindex]
else:
foundattpol = not_applicable
foundgralab = not_applicable
foundgrapol = not_applicable
elif layer == 'gra':
if 'gralab' in founddf.columns:
foundgralab = founddf['gralab'].tolist()[0].split('|')[labelindex]
else:
foundgralab = not_applicable
if 'grapol' in founddf.columns:
foundgrapol = founddf['grapol'].tolist()[0].split('|')[labelindex]
else:
foundgrapol = not_applicable
foundattlab = not_applicable
foundattpol = not_applicable
elif layer == 'neg':
if 'negation' in founddf.columns:
foundneglab = founddf['negation'].tolist()[0].split('|')[labelindex]
else:
foundneglab = not_applicable
else:
print(label, "I can't tell which label this is.")
# put all that together into a list for a new row
if layer == 'att' or layer == 'gra':
foundrow = [commentid, foundsentstart, foundsentend, foundcharstart, foundcharend,
foundwords, foundattlab, foundattpol, foundgralab, foundgrapol]
elif layer == 'neg':
foundrow = [commentid, foundsentstart, foundsentend, foundcharstart, foundcharend,
foundwords, foundneglab]
else:
print("I couldn't make a new row because I don't know which label this is")
# add that row to foundspans
foundspans.append(foundrow)
if verbose:
print('Done with comment', commentid, "label", label)
return foundspans
else:
print('Your label was not recognized')
# more info to help process the TSVs
sentence_indicator = '#Text=' # what WebAnno TSVs have written to indicate the full text of a sentence
sent_startpos = len(sentence_indicator) # where the text for a sentence would actually start
name_indicator = '#comment: ' # text added to the combined tsv to indicate the name of each comment
name_startpos = len(name_indicator) # where the text for the comment name would actually start
# set up the data frame to be filled in
new_df_columns = ('comment',
'comment_counter',
'sentstart',
'sentend',
'charstart',
'charend',
'span',
'attlab',
'attpol',
'gralab',
'grapol',
'neglab',)
new_df = pd.DataFrame(columns=new_df_columns)
# combine negation and Appraisal annotations
if appraisal_comments_path and negation_comments_path:
# Do stuff with appraisal
print('Processing appraisal comments')
new_appraisal_df = new_df
sentences_df = pd.DataFrame()
for comment in appraisal_comments_list:
if comment: # due to how the comments list is made, it ends in a '' which can't be read properly
# extract sentences and clean them
linelist = comment.split('\n')
clean_comment_lines = []
sentences = []
for line in linelist:
# get the sentences and comment names and discard other commented lines
if re.match('#', line): # if WebAnno commented out the line
if re.match(sentence_indicator, line): # if the line has the text for a sentence
# add that sentence to the sentences list
if not lastmatch_sent:
# Normally, each line with '#Text=' is a new sentence. Add the relevant part to the list:
sentences.append(line[sent_startpos:])
else:
# for some reason aboriginal_11 has 2 '#Text=' lines, but only one sentence.
# This conditional addresses that
sentences[-1] = sentences[-1] + line[sent_startpos:]
lastmatch_sent = True
else:
lastmatch_sent=False
if re.match(name_indicator, line): # if the line has the text for a comment name
# set the names
oldname = line[name_startpos:]
if mappingdict1 or mappingdict2:
# get the comment_counter name - note that file extensions may vary between .txt and .tsv
# because of how files were managed in WebAnno, hence the replacement of the extension
newname = oldname[:-4] + '.txt'
if newname in mappingdict1:
newname = mappingdict1[newname]
elif newname in mappingdict2:
newname = mappingdict2[newname]
# if the line was not commented out, put it in the new "clean" list:
else:
clean_comment_lines.append(line)
# put the sentences into a df and add them to the existing df
new_sentences_df = pd.DataFrame()
new_sentences_df['span'] = sentences
print('Processing comment', oldname)
# put the comment into a pandas df
clean_comment = '\n'.join(clean_comment_lines)
clean_comment_buffer = StringIO(clean_comment)
clean_df = readprojfile(clean_comment_buffer, 'app')
# Find which labels occur in the sentence
labinds = getlabinds_df(clean_df, appraisal_collabels)
foundrows = []
for i in range(len(appraisal_collabels)):
searchcolumn = appraisal_collabels[i][0] # which column to look in
if searchcolumn in clean_df.columns:
searchlabels = labinds[searchcolumn] # which labels to look for in that column
for searchlabel in searchlabels:
if searchlabel != '_': # don't include blank spans
foundstuff = lookup_label(clean_df,
searchcolumn,
searchlabel,
commentid=oldname,
not_applicable=blank_entry,
verbose=False)
if '[' in searchlabel: # in this case, foundstuff is one row of data
foundrows.append(foundstuff)
else: # in this case, foundstuff is many rows of data
for row in foundstuff:
foundrows.append(row)
# make a dataframe with all the info we just added so that we can add it to the master df
foundrows_df = pd.DataFrame(foundrows, columns=('comment',
'sentstart',
'sentend',
'charstart',
'charend',
'span',
'attlab',
'attpol',
'gralab',
'grapol',))
# add in the comment_counter name
if mappingdict1 or mappingdict2:
foundrows_df['comment_counter'] = newname
# flesh out the new sentences df
for column in new_df_columns:
if column in ('charstart', 'charend', 'attlab', 'attpol', 'gralab', 'grapol', 'neglab'):
new_sentences_df[column] = blank_entry
elif column in ('sentstart', 'sentend'):
# fill in the sentence positions
new_sentences_df[column] = [i for i in range(1, len(sentences) + 1)]
elif column == 'comment':
new_sentences_df[column] = oldname
elif column == 'comment_counter':
new_sentences_df[column] = newname
# we want to include character positions
# read the TSV and make a new quick DF of sentences and characters
# this will let us find which character #'s a sentence starts and ends with
sentchar_df = pd.DataFrame(columns=('sentence', 'charstart', 'charend'))
# prepare "sentence" column
sentence_numbers = []
for i in clean_df['sentpos'].tolist():
# find the number before a hyphen, return that number, then coerce it into an integer and add it to
# sentence_numbers
sentence_numbers.append(int(re.search(r'^.*-', i).group()[:-1]))
sentchar_df['sentence'] = sentence_numbers
# prepare "charstart" and "charend" columns
charstarts = []
charends = []
for i in clean_df['charpos'].tolist():
charstarts.append(int(re.search(r'^.*-', i).group()[:-1]))
charends.append(int(re.search(r'-.*$', i).group()[1:]))
sentchar_df['charstart'] = charstarts
sentchar_df['charend'] = charends
# add in character position info to the dataframe
new_sents_charstarts = []
new_sents_charends = []
for i in new_sentences_df['sentstart']:
startlist = sentchar_df.loc[sentchar_df.sentence == i, 'charstart'].tolist()
endlist = sentchar_df.loc[sentchar_df.sentence == i, 'charend'].tolist()
new_sentences_df.loc[new_sentences_df.sentstart == i, 'charstart'] = min(startlist)
new_sentences_df.loc[new_sentences_df.sentstart == i, 'charend'] = max(endlist)
sentences_df = sentences_df.append(new_sentences_df)
# add this comment's information to the Appraisal df
foundrows_df['neglab'] = blank_entry
new_appraisal_df = new_appraisal_df.append(foundrows_df)
# Do the same for negation
print('Processing negation comments')
new_negation_df = new_df
for comment in negation_comments_list:
if comment: # due to how the comments list is made, it ends in a '' which can't be read properly
# extract sentences and clean them
linelist = comment.split('\n')
clean_comment_lines = []
# sentences list not needed since we have this from Appraisal
for line in linelist:
# get the sentences and comment names and discard other commented lines
if re.match('#', line): # if WebAnno commented out the line
if re.match(name_indicator, line): # if the line has the text for a comment name
# set the name
oldname = line[name_startpos:]
# if the line was not commented out, put it in the new "clean" list:
else:
clean_comment_lines.append(line)
print('Processing comment', oldname)
# put the comment into a pandas df
clean_comment = '\n'.join(clean_comment_lines)
clean_comment_buffer = StringIO(clean_comment)
clean_df = readprojfile(clean_comment_buffer, 'neg')
# Find which labels occur in the sentence
labinds = getlabinds_df(clean_df, negation_collabels)
foundrows = []
for i in range(len(negation_collabels)):
searchcolumn = negation_collabels[i][0] # which column to look in
if searchcolumn in clean_df.columns:
searchlabels = labinds[searchcolumn] # which labels to look for in that column
for searchlabel in searchlabels:
if searchlabel != '_': # don't include blank spans
foundstuff = lookup_label(clean_df,
searchcolumn,
searchlabel,
commentid=oldname,
not_applicable=blank_entry,
verbose=False)
if '[' in searchlabel: # in this case, foundstuff is one row of data
foundrows.append(foundstuff)
else: # in this case, foundstuff is many rows of data
for row in foundstuff:
foundrows.append(row)
# make a dataframe with all the info we just added so that we can add it to the master df
foundrows_df = pd.DataFrame(foundrows, columns=('comment',
'sentstart',
'sentend',
'charstart',
'charend',
'span',
'neglab',))
# add in the comment_counter name
if mappingdict1 or mappingdict2:
foundrows_df['comment_counter'] = newname
# make blank columns to smooth the appending process
for column in ('attlab', 'attpol', 'gralab', 'grapol',):
foundrows_df[column] = blank_entry
new_negation_df = new_df.append(foundrows_df)
# combine all those dataframes
new_df = new_df.append(new_appraisal_df)
new_df = new_df.append(new_negation_df)
new_df = new_df.append(sentences_df)
# sort by which character the row starts with, then which character it ends at
# this means that it will read chronologically, with longer spans appearing first
new_df = new_df.sort_values(by=['comment', 'charstart', 'charend'],
ascending=[True, True, False])
else:
print("Can't combine only one project.")
# add in the constructiveness and toxicity annotations
if contox_path:
# make empty columns in new_df
contox_columns = ('is_constructive', 'is_constructive:confidence', 'toxicity_level', 'toxicity_level:confidence')
for column in contox_columns:
new_df[column] = 'error'
contox_df = pd.read_csv(contox_path)
length_error = [] # in case there are any duplicate rows in the contox_df
for column in contox_columns:
for comment in contox_df['comment_counter']:
if len(contox_df.loc[contox_df.comment_counter == comment, column]) > 1:
length_error.append(comment)
print('Length error for', comment)
print('Adding', column, 'to', comment)
new_df.loc[new_df.comment_counter == comment, column] = \
contox_df.loc[contox_df.comment_counter == comment, column].tolist()[0]
# .tolist()[0] was added to get a straightforward string out of the relevant entry
if length_error:
print('Duplicate rows found. You may want to check these comments:')
for i in length_error:
print(i)
else:
print('Not adding constructiveness and toxicity as no path to it was given.')
# write the df
if writepath:
new_df.to_csv(writepath)
print('New dataframe written to', writepath)
else:
print('Not writing to file as no file path was specified.') | 43,711 | 54.261694 | 119 | py |
SOCC | SOCC-master/scripts/old_combine_comments.py | from glob import glob
import pandas as pd
import re
# where to find your cleaned TSVs:
appraisal_projectpath = input('Path to appraisal project folder: (e.g. C:/.../Appraisal/clean_TSVs)')
# where to write a new CSV
appraisal_writepath = input('Path to write a new appraisal CSV to: (e.g. C:/.../combined_appraisal_comments.csv)')
# same for negation
negation_projectpath = input('Path to negation project folder: (e.g. C:/.../Negation/clean_TSVs)')
negation_writepath = input('Path to write a new negation CSV to: (e.g. C:/.../combined_negation_comments.csv)')
# where to find the mapping CSV so that names like source_x_x and aboriginal_1 are both used:
mapping_csv = input("path to your mapping of names e.g. 'C:/.../comment_counter_appraisal_mapping.csv'")
# change these variables if you are not using appraisal annotations
# they are the actual column headers for the TSV files
# some Appraisal TSVs do not have graduation, hence the need for two lists of names
appraisal_longheaders = ['sentpos', 'charpos', 'word', 'attlab', 'attpol', 'gralab', 'grapol']
appraisal_shortheaders = ['sentpos', 'charpos', 'word', 'attlab', 'attpol']
negation_headers = ['sentpos', 'charpos', 'word', 'negation']
# some comments have no annotations
no_annotation = ['sentpos', 'charpos', 'word']
appraisal_possnames = [no_annotation, appraisal_shortheaders, appraisal_longheaders]
negation_possnames = [no_annotation, negation_headers]
def getcontents(directory):
"""
Returns the file paths for all files in the specified path (directory). Identical to glob.glob() except that it
converts '\\' to '/'
"""
return [name.replace('\\', '/') for name in glob(directory + '/*')]
appraisal_projectdirs = getcontents(appraisal_projectpath)
negation_projectdirs = getcontents(negation_projectpath)
def readprojfile(path, project):
"""
Reads a cleaned WebAnno TSV into a pandas dataframe. One column is often read as full of NaN's due to the TSVs'
original formatting, so this function drops any columns with NaN's.
:param path: the path to the TSV
:param possnames: the headers that may occur in the TSV, as a list of lists of headers.
The function will check each list within possnames to see if its length is equal to the number of columns
:param project: 'app' if Appraisal, 'neg' if negation.
:return: a pandas dataframe containing the information in the original TSV
"""
# set possnames
if project == "neg" or project.lower() == "negation":
possnames = negation_possnames
project = "neg"
elif project == "app" or project.lower() == "appraisal":
possnames = appraisal_possnames
project = "app"
else:
print("Project type not recognized. Use 'neg' or 'att'.")
possnames = None
newdf = pd.read_csv(path, sep='\t', header=None)
newdf = newdf.dropna(axis=1, how='all')
if (project == "neg" or project.lower() == "negation")\
and len(newdf.columns) == 5: # Neg annotations with arrows have an extra column we won't use
newdf = newdf.loc[:, 0:3] # so we'll just delete it
for headers in possnames:
if len(newdf.columns) == len(headers):
newdf.columns = headers
if all([len(newdf.columns) != i for i in [len(headers) for headers in possnames]]):
print("No correct number of columns in", path)
return newdf
attlabs = ('Appreciation', 'Affect', 'Judgment')
attpols = ('pos', 'neu', 'neg')
gralabs = ('Force', 'Focus')
grapols = ('up', 'down')
neglabs = ('NEG', 'SCOPE', 'FOCUS', 'XSCOPE')
# create a list to show which column to look in and which labels to look for in that column
appraisal_collabels = ((appraisal_longheaders[3], attlabs),
(appraisal_longheaders[4], attpols),
(appraisal_longheaders[5], gralabs),
(appraisal_longheaders[6], grapols))
# this next tuple is within another tuple so that the same commands we need later will iterate correctly
negation_collabels = (('negation', neglabs),)
# create a dictionary matching old comment names to comment counter ones
if mapping_csv:
mapping1 = pd.read_csv(mapping_csv)
list1 = mapping1['appraisal_negation_annotation_file_name'].tolist()
list2 = mapping1['comment_counter'].tolist()
# dictionary of original to comment counter names
mappingdict1 = {}
for i in range(max(len(list1), len(list2))):
mappingdict1[list1[i]] = list2[i]
# same dictionary in reverse
mappingdict2 = {}
for i in range(max(len(list1), len(list2))):
mappingdict2[list2[i]] = list1[i]
def getlabinds(dataframe, correspondences, dfname="dataframe", verbose=False):
"""
Gets the unique labels, including indices, that appear in a dataframe so that they can be searched later.
:param dataframe: a pandas dataframe
:param correspondences: a list or tuple of columns and labels like collabels
:param dfname: a name for the dataframe, used for reporting when one or more columns doesn't show up
:param verbose: a boolean; if True, tells you when a dataframe is missing a column
:return: a list of the form [(index of column),(list of unique labels including index of that label, e.g.
['Appreciation','Appreciation[1]','Appreciation[2]'])
"""
newdict = {}
for entry in range(len(correspondences)):
if correspondences[entry][0] in dataframe.columns:
searchedlist = dataframe[correspondences[entry][0]].tolist()
splitlist = [i.split('|') for i in searchedlist]
foundlist = []
for e in splitlist: # each element in splitlist is currently a list
for i in e: # so i is a string
foundlist.append(i) # so now foundlist is a list of strings
foundlist = set(foundlist) # convert to set so we have uniques only
foundlist = [label for label in foundlist] # convert foundlist back to a list
newdict[correspondences[entry][0]] = foundlist
else:
if verbose:
print(dfname, "does not include column", correspondences[entry][0])
return newdict
def listand(list1, list2):
"""
Returns a new list, applying the "and" operation to each item pairwise in list 1 and 2
:param list1: A list
:param list2: A second list
:return: A list of booleans
"""
return [a and b for a, b in zip(list1, list2)]
def lookup_label(dataframe, column, label, commentid="dataframe", not_applicable=None, verbose=False,
clean_suffix='_cleaned.tsv'):
"""
Looks in the dataframe for rows matching the label and returns them.
:param dataframe: A pandas dataframe
:param column: which column in the dataframe to look in for the labels
:param label: which label to look for in the column
:param commentid: the name of the comment; the new row will have this as its first entry
:param bothids: whether to include both comment names (e.g. aboriginal_1 and source_xx_xx)
:param not_applicable: what to put in a cell if there is no data (e.g. something un-annotated)
:param verbose: whether to tell you when it's done
:param clean_suffix: the suffix appended to clean files. Default assumes you cleaned them with clean_comments.py
:return: a list that can be used as a new row or rows. If the label has no index (e.g. 'Appreciation' or '_'), then
all rows with those labels will be returned. If it has an index (e.g. 'Appreciation[3]'), then one row
representing that annotated span will be returned.
The fields in the list are, by column:
- the comment ID
- which sentence the span starts in
- which sentence it ends in
- which character it starts on
- which character it ends on
- which words are in the span
- the Attitude label for the span
- the Attitude polarity for the span
- the graduation label for the span
- the graduation polarity for the span
"""
# determine if we're looking at attitude, graduation, or negation
if 'att' in column:
layer = 'att'
elif 'gra' in column:
layer = 'gra'
elif column == 'negation':
layer = 'neg'
else:
layer = 'unknown'
# Check that both label and polarity columns are present
if ('attlab' in dataframe.columns) ^ ('attpol' in dataframe.columns):
if 'attlab' in dataframe.columns:
print(commentid, 'has attlab column but no attpol column')
if 'attpol' in dataframe.columns:
print(commentid, 'has attpol column but no attlab column')
if ('gralab' in dataframe.columns) ^ ('grapol' in dataframe.columns):
if 'gralab' in dataframe.columns:
print(commentid, 'has gralab column but no grapol column')
if 'grapol' in dataframe.columns:
print(commentid, 'has grapol column but no gralab column')
# look for labels with brackets (e.g. 'Appreciation[3]')
if '[' in label:
mask = [(label in i) for i in dataframe[column].tolist()]
founddf = dataframe[mask]
# get the sentence(s) of the label
foundsentstart = int(re.search(r'^.*-', founddf['sentpos'].tolist()[0]).group()[:-1])
foundsentend = int(re.search(r'^.*-', founddf['sentpos'].tolist()[-1]).group()[:-1])
# get the character positions for the new row
# look at which character the label starts in
foundcharstart = int(re.search(r'^.*-', founddf['charpos'].tolist()[0]).group()[:-1])
# look at which character the label ends in
foundcharend = int(re.search(r'-.*$', founddf['charpos'].tolist()[-1]).group()[1:])
# concatenate the words for the new row
foundwords = ''
for word in founddf['word']:
foundwords = foundwords + word + ' '
foundwords = foundwords[:-1]
# get the labels for the new row
# in case of pipes, figure out which one is the real label
posslabels = founddf[column].tolist()
posslabels = posslabels[0]
posslabels = posslabels.split('|')
labelindex = posslabels.index(label)
# now look through the columns and find the appropriate labels
# Each column is converted to a list. The first item in the list is used to find the label.
# This item is split by '|' in case of stacked annotations.
# Before, we found the index of the label we want. We get the found label from this index.
if layer == 'att':
if 'attlab' in founddf.columns:
foundattlab = founddf['attlab'].tolist()[0].split('|')[labelindex]
# We want to cut off the index (e.g. 'Appreciation[3]' -> 'Appreciation')
# search() finds everything up to the '[', and .group()[:-1] returns what it found, minus the '['
foundattlab = re.search(r'^.*\[', foundattlab).group()[:-1]
else:
foundattlab = not_applicable
if 'attpol' in founddf.columns:
foundattpol = founddf['attpol'].tolist()[0].split('|')[labelindex]
foundattpol = re.search(r'^.*\[', foundattpol).group()[:-1]
else:
foundattpol = not_applicable
foundgralab = not_applicable
foundgrapol = not_applicable
elif layer == 'gra':
if 'gralab' in founddf.columns:
foundgralab = founddf['gralab'].tolist()[0].split('|')[labelindex]
foundgralab = re.search(r'^.*\[', foundgralab).group()[:-1]
else:
foundgralab = not_applicable
if 'grapol' in founddf.columns:
foundgrapol = founddf['grapol'].tolist()[0].split('|')[labelindex]
foundgrapol = re.search(r'^.*\[', foundgrapol).group()[:-1]
else:
foundgrapol = not_applicable
foundattlab = not_applicable
foundattpol = not_applicable
elif layer == 'neg':
if 'negation' in founddf.columns:
foundneglab = founddf['negation'].tolist()[0].split('|')[labelindex]
foundneglab = re.search(r'^.*\[', foundneglab).group()[:-1]
else:
foundneglab = not_applicable
else:
print(label, "I can't tell which label this is.")
# put all that together into a list for a new row
if layer == 'att' or layer == 'gra':
foundrow = [commentid, foundsentstart, foundsentend, foundcharstart, foundcharend,
foundwords, foundattlab, foundattpol, foundgralab, foundgrapol]
elif layer == 'neg':
foundrow = [commentid, foundsentstart, foundsentend, foundcharstart, foundcharend,
foundwords, foundneglab]
else:
print("I couldn't make a new row because I don't know which label this is")
if verbose:
print('Done with comment', commentid, "label", label)
return foundrow
# look for unlabelled spans (i.e. label '_')
elif label == '_':
if layer == 'neg':
mask = [(label in i) for i in dataframe[column].tolist()]
founddf = dataframe[mask]
# If the layer is Attitude or Graduation, check for spans with a label but no polarity or vice versa
# and be sure that any spans returned as unlabelled have no label or polarity
elif layer == 'att' or layer == 'gra':
attmask = []
gramask = []
if 'attlab' in dataframe.columns and 'attpol' in dataframe.columns:
mask1 = [(label in i) for i in dataframe['attlab'].tolist()]
mask2 = [(label in i) for i in dataframe['attpol'].tolist()]
for i in range(len(mask1)):
if mask1[i] is not mask2[i]:
print('row', i, 'has mismatched Attitude labels')
attmask = [a and b for a, b in zip(mask1, mask2)]
if 'gralab' in dataframe.columns and 'grapol' in dataframe.columns:
mask3 = [(label in i) for i in dataframe['gralab'].tolist()]
mask4 = [(label in i) for i in dataframe['grapol'].tolist()]
for i in range(len(mask3)):
if mask3[i] is not mask4[i]:
print('row', i, 'has mismatched Graduation labels')
gramask = [a and b for a, b in zip(mask3, mask4)]
if attmask and not gramask:
mask = attmask
elif gramask and not attmask:
mask = gramask
elif attmask and gramask:
mask = [a and b for a, b in zip(attmask, gramask)]
elif not attmask and not gramask: # this will return all rows if there's no attlab or
mask = [True for i in range(len(dataframe))] # gralab, since there's no annotations at all.
founddf = dataframe[mask]
else:
print("Layer unrecognized when looking for unlabelled spans")
# find the sentences
sentences = []
for i in range(len(founddf['sentpos'])):
sentences.append(
int( # we want to do math on this later
re.search(
r'^.*-', founddf['sentpos'].tolist()[i] # finds whatever comes before a '-'
).group()[:-1] # returns the string it found
))
# find the character positions
charpositions = []
for i in range(len(founddf['charpos'])):
charpositions.append(
(int(re.search(r'^.*-', founddf['charpos'].tolist()[i]).group()[:-1]),
int(re.search(r'-.*$', founddf['charpos'].tolist()[i]).group()[1:]))
)
# find all the words
allfoundwords = founddf['word'].tolist()
# find consecutive unlabelled words
foundspans = []
span_number = -1
last_match = False
for i in range(len(allfoundwords)):
if i - 1 in range(len(allfoundwords)): # if this isn't the first word
# check if this word came right after the last one
if sentences[i - 1] == sentences[i] and\
(charpositions[i - 1][-1] == (charpositions[i][0] - 1) or\
charpositions[i - 1][-1] == (charpositions[i][0])):
if not last_match: # if this is not a continuation of the previous span
span_number += 1 # keep track of the number we're on (index of foundspans)
# add the row for this span to foundspans
if layer == 'att' or layer == 'gra':
foundspans.append([commentid, # comment ID
sentences[i-1], # sentence start
sentences[i], # sentence end
charpositions[i-1][0], # character start
charpositions[i][-1], # character end
allfoundwords[i - 1] + ' ' + allfoundwords[i], # words
not_applicable, # Labels are all assumed to be absent.
not_applicable, # Per earlier code, it should tell you if that is
not_applicable, # not actually the case.
not_applicable, ])
elif layer == 'neg':
foundspans.append([commentid, # comment ID
sentences[i-1], # sentence start
sentences[i], # sentence end
charpositions[i-1][0], # character start
charpositions[i][-1], # character end
allfoundwords[i - 1] + ' ' + allfoundwords[i], # words
not_applicable])
last_match = True # record these two i's as contiguous
else: # (this word is a continuation of the previous span)
foundspans[span_number].pop(4) # remove the ending char position so we can replace it
oldwords = foundspans[span_number].pop(4) # remove the words from the span to replace it
foundspans[span_number].insert(4, charpositions[i][-1]) # add the last character of this word
foundspans[span_number].insert(5, oldwords + ' ' + allfoundwords[i]) # add the words together
else:
last_match = False # record these two i's as non-contiguous
# check if this is the first pair of words we're looking at
if i == 1: # i would equal 1 bc we skip i=0 (since we looked backwards)
# if i=1 and the first and second words are non-contiguous, we need to add
# the first word to foundspans.
if layer == 'att' or layer == 'gra':
foundspans.append([commentid, # comment ID
sentences[i-1], # sentence start
sentences[i-1], # sentence end
charpositions[i-1][0], # character start
charpositions[i-1][-1], # character end
allfoundwords[i-1], # word
not_applicable, # Labels are all assumed to be absent.
not_applicable, # Per earlier code, it should tell you if that is
not_applicable, # not actually the case.
not_applicable, ])
elif layer == 'neg':
foundspans.append([commentid, # comment ID
sentences[i-1], # sentence start
sentences[i-1], # sentence end
charpositions[i-1][0], # character start
charpositions[i-1][-1], # character end
allfoundwords[i-1], # word
not_applicable])
# look ahead to see if the next word is a continuation of this span:
if i + 1 in range(len(sentences)):
if sentences[i + 1] != sentences[i] and charpositions[i + 1][-1] != (charpositions[i][0] + 1):
span_number = span_number + 1 # if so, keep track of the index
if layer == 'att' or layer == 'gra':
foundspans.append([commentid, # comment ID
sentences[i], # sentence start
sentences[i], # sentence end
charpositions[i][0], # character start
charpositions[i][-1], # character end
allfoundwords[i], # word
not_applicable, # Labels are all assumed to be absent.
not_applicable, # Per earlier code, it should tell you if that is
not_applicable, # not actually the case.
not_applicable, ])
elif layer == 'neg':
foundspans.append([commentid, # comment ID
sentences[i], # sentence start
sentences[i], # sentence end
charpositions[i][0], # character start
charpositions[i][-1], # character end
allfoundwords[i], # word
not_applicable])
# else: the loop continues
else: # if there is no following word and this one isn't a continuation, it's its own word.
span_number = span_number + 1
if layer == 'att' or layer == 'gra':
foundspans.append([commentid, # comment ID
sentences[i], # sentence start
sentences[i], # sentence end
charpositions[i][0], # character start
charpositions[i][-1], # character end
allfoundwords[i], # word
not_applicable, # Labels are all assumed to be absent.
not_applicable, # Per earlier code, it should tell you if that is
not_applicable, # not actually the case.
not_applicable, ])
elif layer == 'neg':
foundspans.append([commentid, # comment ID
sentences[i], # sentence start
sentences[i], # sentence end
charpositions[i][0], # character start
charpositions[i][-1], # character end
allfoundwords[i], # word
not_applicable]) # no negation
if verbose:
print('Done with comment', commentid, "label", label)
return foundspans
# look for one-word annotated spans (e.g. 'Appreciation'
elif ((label in attlabs) or
(label in attpols) or
(label in gralabs) or
(label in grapols) or
(label in neglabs)):
# create subset dataframe - stricter than other conditions
mask = [(label == i) for i in dataframe[column].tolist()]
founddf = dataframe[mask]
# find the sentences
sentences = []
for i in range(len(founddf['sentpos'])):
sentences.append(
int( # we want to do math on this later
re.search(
r'^.*-', founddf['sentpos'].tolist()[i] # finds whatever comes before a '-'
).group()[:-1] # returns the string it found, minus 1 character from the end
))
# find the character positions
charpositions = []
for i in range(len(founddf['charpos'])):
charpositions.append(
(int(re.search(r'^.*-', founddf['charpos'].tolist()[i]).group()[:-1]),
int(re.search(r'-.*$', founddf['charpos'].tolist()[i]).group()[1:]))
)
# find the words
allfoundwords = founddf['word'].tolist()
# in case of pipes, figure out which one is the real label
posslabels = founddf[column].tolist()
posslabels = posslabels[0]
posslabels = posslabels.split('|')
labelindex = posslabels.index(label)
# now look through the columns and find the appropriate labels
# Each column is converted to a list. The first item in the list is used to find the label.
# This item is split by '|' in case of stacked annotations.
# Before, we found the index of the label we want. We get the found label from this index.
foundspans = []
for i in range(len(founddf)):
# since these are one word long, the starting and ending sentences are the same.
foundsentstart = sentences[i]
foundsentend = foundsentstart
# find the characters the word starts and ends with
foundcharstart = charpositions[i][0]
foundcharend = charpositions[i][1]
# find the word
foundwords = allfoundwords[i]
if layer == 'att':
if 'attlab' in founddf.columns:
foundattlab = founddf['attlab'].tolist()[0].split('|')[labelindex]
else:
foundattlab = not_applicable
if 'attpol' in founddf.columns:
foundattpol = founddf['attpol'].tolist()[0].split('|')[labelindex]
else:
foundattpol = not_applicable
foundgralab = not_applicable
foundgrapol = not_applicable
elif layer == 'gra':
if 'gralab' in founddf.columns:
foundgralab = founddf['gralab'].tolist()[0].split('|')[labelindex]
else:
foundgralab = not_applicable
if 'grapol' in founddf.columns:
foundgrapol = founddf['grapol'].tolist()[0].split('|')[labelindex]
else:
foundgrapol = not_applicable
foundattlab = not_applicable
foundattpol = not_applicable
elif layer == 'neg':
if 'negation' in founddf.columns:
foundneglab = founddf['negation'].tolist()[0].split('|')[labelindex]
else:
foundneglab = not_applicable
else:
print(label, "I can't tell which label this is.")
# put all that together into a list for a new row
if layer == 'att' or layer == 'gra':
foundrow = [commentid, foundsentstart, foundsentend, foundcharstart, foundcharend,
foundwords, foundattlab, foundattpol, foundgralab, foundgrapol]
elif layer == 'neg':
foundrow = [commentid, foundsentstart, foundsentend, foundcharstart, foundcharend,
foundwords, foundneglab]
else:
print("I couldn't make a new row because I don't know which label this is")
# add that row to foundspans
foundspans.append(foundrow)
if verbose:
print('Done with comment', commentid, "label", label)
return foundspans
else:
print('Your label was not recognized')
# you can try commands like:
"""
testdf1 = readprojfile(appraisal_projectdirs[3], 'app')
lookup_label(testdf1,'attlab','_', commentid='testdf1')
lookup_label(testdf1,'attlab','Judgment[4]', commentid='testdf1')
lookup_label(testdf1,'attlab','Appreciation', commentid='testdf1')
lookup_label(testdf1, 'gralab', 'Force', commentid='testdf1')
testdf2 = readprojfile(negation_projectdirs[3], 'neg')
lookup_label(testdf2, 'negation', 'NEG', commentid='testdf2')
lookup_label(testdf2, 'negation', 'SCOPE[2]', commentid='testdf2')
lookup_label(testdf2, 'negation', '_', commentid='testdf2')
"""
# this variable will be used in a moment; it's the same as collabels, keeping only the 'label' parts
# it's used so that we don't search polarity redundantly
appraisal_search_correspondences = (appraisal_collabels[0], appraisal_collabels[2])
# column names for new dataframes:
appraisal_newheads = ['comment',
'sentstart',
'sentend',
'charstart',
'charend',
'span',
'attlab',
'attpol',
'gralab',
'grapol']
negation_newheads = ['comment',
'sentstart',
'sentend',
'charstart',
'charend',
'span',
'label']
def simplify_dataframe(dataframe, project, commentid="Dataframe", not_applicable=None, bothids=True,
clean_suffix='_cleaned.tsv', verbose=()):
"""
Uses all the labels in correspondences to create a new dataframe organized by span rather than by word.
:param dataframe: the dataframe to search and re-create
:param project: 'neg' for a negation project, 'app' for an appraisal project
:param commentid: the name of the comment; the new row will have this as its first entry
:param not_applicable: what to put in a cell if there is no data (e.g. something un-annotated)
:param bothids: whether to add in a column with the other id (e.g. aboriginal_1 or source_01...)
:param clean_suffix: the suffix added to clean files (this will be removed from commentid to find the other id)
:param verbose: an iterable containing one or more of the following strings:
missingcol: reports whenever a comment lacks an annotation for one or more columns
label_done: reports when each label has been searched for (same as verbose for lookup_label)
comment_done: reports when the function has finished running
:return: a new dataframe with the same content as the one given in the first place, but reorganized by span
rather than by word
"""
# set verbosity
if 'missingcol' in verbose:
verbose_missingcol = True
else:
verbose_missingcol = False
# set newcols and correspondences
if project == "neg" or project.lower() == "negation":
newcols = negation_newheads
correspondences = negation_collabels
project = "neg"
elif project == "app" or project.lower() == "appraisal":
newcols = appraisal_newheads
correspondences = appraisal_search_correspondences
project = "app"
else:
print("Project type not recognized. Use 'neg' or 'app'.")
newcols = None
correspondences = None
# find the labels to look for
labinds = getlabinds(dataframe, correspondences=correspondences, dfname=commentid, verbose=verbose_missingcol)
# search the old dataframe and create a list to later add as rows to the empty one
if 'label_done' in verbose:
v_label_done = True
else:
v_label_done = False
foundrows = []
for i in range(len(correspondences)):
searchcolumn = correspondences[i][0] # which column to look in
if searchcolumn in dataframe.columns:
searchlabels = labinds[searchcolumn] # which labels to look for in that column
for searchlabel in searchlabels:
foundstuff = lookup_label(dataframe,
searchcolumn,
searchlabel,
commentid=commentid,
not_applicable=not_applicable,
verbose=v_label_done)
if '[' in searchlabel: # in this case, foundstuff is one row of data
foundrows.append(foundstuff)
else: # in this case, foundstuff is many rows of data
for row in foundstuff:
foundrows.append(row)
# if foundrows is empty, then instead of returning an empty df, return a df with a None-annotated row.
if not foundrows:
# find the sentences
sentences = []
# add the first sentence number to sentences
sentences.append(
int(
re.search(
r'^.*-', dataframe['sentpos'].tolist()[0] # finds whatever comes before a '-'
).group()[:-1] # returns the string it found
))
# add the last sentence number to sentences
sentences.append(
int(
re.search(
r'^.*-', dataframe['sentpos'].tolist()[-1] # finds whatever comes before a '-'
).group()[:-1] # returns the string it found
))
# find the character positions
charpositions = (int(re.search(r'^.*-', dataframe['charpos'].tolist()[0]).group()[:-1]),
int(re.search(r'-.*$', dataframe['charpos'].tolist()[-1]).group()[1:]))
# find all the words
allfoundwords = dataframe['word'].tolist()
allfoundwords = " ".join(allfoundwords)
if project == "app":
foundrows.append([commentid, # comment ID
sentences[0], # sentence start
sentences[1], # sentence end
charpositions[0], # character start
charpositions[1], # character end
allfoundwords, # word
not_applicable, # Labels are all assumed to be absent.
not_applicable,
not_applicable,
not_applicable, ])
elif project == "neg":
foundrows.append([commentid, # comment ID
sentences[0], # sentence start
sentences[1], # sentence end
charpositions[0], # character start
charpositions[1], # character end
allfoundwords, # word
not_applicable]) # no negation
# make the rows into a new df
newdf = pd.DataFrame(foundrows, columns=newcols)
# sort by which character the row starts with in ascending order, then which character it ends with descending
# this means that it will read chronologically, with longer spans appearing first
newdf = newdf.sort_values(by=['charstart', 'charend'], ascending=[True, False])
# lookup_label will return duplicate rows when there is a span annotated only for Attitude or only for Graduation.
newdf = newdf.drop_duplicates()
# now, if necessary, add a column for the other comment id
if bothids:
commentid = commentid[:-len(clean_suffix)] + '.txt'
if commentid in mappingdict1:
otherid = mappingdict1[commentid]
elif commentid in mappingdict2:
otherid = mappingdict2[commentid]
else:
otherid=''
print("No other comment id found for", commentid + '.')
if otherid:
newdf['comment_counter'] = otherid
if 'comment_done' in verbose:
print(commentid, "processed")
return newdf
# try simplify_dataframe(testdf1, appraisal_newheads, appraisal_search_correspondences, commentid="testdf1")
def combine_annotations(paths, project, not_applicable=None, bothids=True, clean_suffix='_cleaned.tsv', verbose=()):
"""
Takes cleaned WebAnno TSVs from given paths and reorganizes them into one single dataframe, with each row
representing a span (not a word, as original TSV rows do).
:param paths: where your cleaned WebAnno TSVs can be found
:param project: 'neg' for a negation project, 'app' for an appraisal project
:param not_applicable: what to put in a cell if there is no data (e.g. something un-annotated)
:param bothids: whether to include a column with the other form of identification (see simplify_dataframe())
:param clean_suffix: the suffix for cleaned files (this is removed if bothids is True, so that the mapping
dictionaries work.) (see simplify_dataframe()).
:param verbose: an iterable containing one or more of the following strings:
missingcol: reports whenever a comment lacks an annotation for one or more columns
label_done: reports when each label has been searched for (same as verbose for lookup_label)
comment_start: before processing a comment, reports which commentid it is about to process
comment_done: reports when the function has finished with each comment
all_done: reports when the function is finished running
:return: A new dataframe incorporating the information of all the TSVs in paths. Each row of the dataframe is one
span.
"""
# set newcols and correspondences
if project.lower() == "neg" or project.lower() == "negation":
newcols = negation_newheads
project = "neg"
elif project.lower() == "app" or project.lower() == "appraisal":
newcols = appraisal_newheads
project = "app"
else:
print("Project type not recognized. Use 'neg' or 'app'.")
newcols = None
newdf = pd.DataFrame(columns=newcols)
for path in paths:
commentid = path.split('/')[-1]
if 'comment_start' in verbose:
print("Processing comment", commentid)
originaldf = readprojfile(path, project)
founddf = simplify_dataframe(originaldf,
project,
commentid=commentid,
bothids=bothids,
clean_suffix=clean_suffix,
not_applicable=not_applicable,
verbose=verbose)
newdf = newdf.append(founddf)
if 'all_done' in verbose:
print("New dataframe created.")
return newdf
# try combine_annotations(testdirs, 'app')
if appraisal_projectpath:
if mapping_csv:
combined_appraisal_dataframe = combine_annotations(appraisal_projectdirs,
'app',
not_applicable='None', # a string works better for R than
verbose=('comment_start',)) # an empty cell
if appraisal_writepath:
combined_appraisal_dataframe.to_csv(appraisal_writepath)
print("Appraisal dataframe exported.")
else:
print("Not exporting Appraisal project as no path was specified.")
else:
combined_appraisal_dataframe = combine_annotations(appraisal_projectdirs,
'app',
not_applicable='None',
bothids=False,
verbose=('comment_start',))
if appraisal_writepath:
combined_appraisal_dataframe.to_csv(appraisal_writepath)
print("Appraisal dataframe exported.")
else:
print("Not exporting Appraisal project as no path was specified.")
else:
print("Not combining Appraisal project as no path was specified.")
if negation_projectpath:
combined_negation_dataframe = combine_annotations(negation_projectdirs,
'neg',
not_applicable='None',
verbose=('comment_start',))
if negation_writepath:
combined_negation_dataframe.to_csv(negation_writepath)
print("Negation dataframe exported.")
else:
print("Not exporting Negation project as no path was specified.")
else:
print("Not combining Negation project as no path was specified.")
| 42,165 | 50.992602 | 119 | py |
SOCC | SOCC-master/scripts/combine_webanno.py | # This script operates directly on the "annotation" folder output by exporting a WebAnno project
# for SOCC, this folder is SOCC\annotated\Appraisal\curation
# Each of \annotation's sub-folders contains a TSV that contains the annotations for the given comment.
# This script puts all of those TSVs into one long file, appending one after the other. In that file,
# commented lines using '#' indicate when the source TSVs begin and end.
import os
from smart_open import smart_open
import re
# path to a folder containing only the WebAnno TSVs
projectpath = input('Path to project folder: (e.g. C:\\...\\curation)')
# directory to output
outputpath = input("Path to write new TSV to: (e.g. 'C:\\...\\newfile.tsv')")
# get the subfolders of /curation
folders = os.listdir(projectpath)
# since all TSVs should be named CURATION_USER.tsv, we need to record the folder name to know which comment is being annotated.
# I use an embedded list for this.
files = [[f, os.listdir(os.path.join(projectpath, f))] for f in folders]
# so for each file 'f' in files, f[0] is the folder that f is contained in, and f[1] is the name of f
# check that each folder contains exactly one CURATION_USER.tsv file
if any([len(f[1]) for f in files]) > 1:
bad_folders = [f[0] for f in files if len(f[1]) > 1]
raise Exception('Some folders have more than one file:', bad_folders)
else:
# since they have exactly one entry each, there's no point in keeping the filename in a list
files = [[f[0], f[1][0]] for f in files]
# check that that file is CURATION_USER.tsv
if any([f[1] != 'CURATION_USER.tsv' for f in files]):
bad_names = [f[1] for f in files if f[1] != 'CURATION_USER.tsv']
raise Exception('Expected files named CURATION_USER.tsv; unexpected file names found:', bad_names)
for f in files:
if f != 'CURATION_USER.tsv':
print(f)
else:
print('Found curated annotations')
# start combining the files
verbose = False # setting this to True may help troubleshooting
newfile = ''
for f in files:
name = f[0]
f_path = os.path.join(projectpath, f[0], f[1])
# indicate the beginning and end of a comment, and what that comment's name is
newfile = newfile + '#comment: ' + name + '\n'
with smart_open(f_path, 'r', encoding='utf-8') as f_io:
newfile = newfile + f_io.read() + '#end of comment\n\n'
if verbose:
print('processed', name)
# output
print('All files processed, writing to', outputpath)
with smart_open(outputpath, 'w') as output:
output.write(newfile)
print('Finished writing.') | 2,604 | 45.517857 | 127 | py |
SOCC | SOCC-master/scripts/webanno_to_span.py | from glob import glob
import pandas as pd
import re
# where to find your cleaned TSVs:
appraisal_projectpath = input('Path to appraisal project folder: (e.g. C:/.../Appraisal/clean_TSVs)')
# where to write a new CSV
appraisal_writepath = input('Path to write a new appraisal CSV to: (e.g. C:/.../combined_appraisal_comments.csv)')
# same for negation
negation_projectpath = input('Path to negation project folder: (e.g. C:/.../Negation/clean_TSVs)')
negation_writepath = input('Path to write a new negation CSV to: (e.g. C:/.../combined_negation_comments.csv)')
# where to find the mapping CSV so that names like source_x_x and aboriginal_1 are both used:
mapping_csv = input("Path to your mapping of names e.g. 'C:/.../comment_counter_appraisal_mapping.csv'")
# these are the actual column headers for the TSV files
# some Appraisal TSVs do not have graduation, hence the need for two lists of names
appraisal_longheaders = ['sentpos', 'charpos', 'word', 'attlab', 'attpol', 'gralab', 'grapol']
appraisal_shortheaders = ['sentpos', 'charpos', 'word', 'attlab', 'attpol']
negation_headers = ['sentpos', 'charpos', 'word', 'negation']
# some comments have no annotations
no_annotation = ['sentpos', 'charpos', 'word']
appraisal_possnames = [no_annotation, appraisal_shortheaders, appraisal_longheaders]
negation_possnames = [no_annotation, negation_headers]
def getcontents(directory):
"""
Returns the file paths for all files in the specified path (directory). Identical to glob.glob() except that it
converts '\\' to '/'
"""
return [name.replace('\\', '/') for name in glob(directory + '/*')]
appraisal_projectdirs = getcontents(appraisal_projectpath)
negation_projectdirs = getcontents(negation_projectpath)
def readprojfile(source, project):
"""
Reads a cleaned WebAnno TSV into a pandas dataframe. One column is often read as full of NaN's due to the TSVs'
original formatting, so this function drops any columns with NaN's.
:param source: the path to the TSV
:param possnames: the headers that may occur in the TSV, as a list of lists of headers.
The function will check each list within possnames to see if its length is equal to the number of columns
:param project: 'app' if Appraisal, 'neg' if negation.
:return: a pandas dataframe containing the information in the original TSV
"""
# set possnames
if project == "neg" or project.lower() == "negation":
possnames = negation_possnames
project = "neg"
elif project == "app" or project.lower() == "appraisal":
possnames = appraisal_possnames
project = "app"
else:
print("Project type not recognized. Use 'neg' or 'att'.")
possnames = None
newdf = pd.read_csv(source, sep='\t', header=None)
newdf = newdf.dropna(axis=1, how='all')
if (project == "neg" or project.lower() == "negation")\
and len(newdf.columns) == 5: # Neg annotations with arrows have an extra column we won't use
newdf = newdf.loc[:, 0:3] # so we'll just delete it
for headers in possnames:
if len(newdf.columns) == len(headers):
newdf.columns = headers
if all([len(newdf.columns) != i for i in [len(headers) for headers in possnames]]):
print("No correct number of columns in", source)
return newdf
# the labels that can show up in different columns
attlabs = ('Appreciation', 'Affect', 'Judgment')
attpols = ('pos', 'neu', 'neg')
gralabs = ('Force', 'Focus')
grapols = ('up', 'down')
neglabs = ('NEG', 'SCOPE', 'FOCUS', 'XSCOPE')
# create some tuples to show which columns go with which labels
appraisal_collabels = ((appraisal_longheaders[3], attlabs),
(appraisal_longheaders[4], attpols),
(appraisal_longheaders[5], gralabs),
(appraisal_longheaders[6], grapols))
# this next tuple is within another tuple so that the same commands we need later will iterate correctly
negation_collabels = (('negation', neglabs),)
# create a dictionary matching old comment names to comment counter ones
if mapping_csv:
mapping1 = pd.read_csv(mapping_csv)
list1 = mapping1['appraisal_negation_annotation_file_name'].tolist()
list2 = mapping1['comment_counter'].tolist()
# dictionary of original to comment counter names
mappingdict1 = {}
for i in range(max(len(list1), len(list2))):
mappingdict1[list1[i]] = list2[i]
# same dictionary in reverse
mappingdict2 = {}
for i in range(max(len(list1), len(list2))):
mappingdict2[list2[i]] = list1[i]
def getlabinds_df(dataframe, correspondences, dfname="dataframe", verbose=False):
"""
Gets the unique labels, including indices, that appear in a dataframe so that they can be searched later.
:param dataframe: a pandas dataframe
:param correspondences: a list or tuple of columns and labels like collabels
:param dfname: a name for the dataframe, used for reporting when one or more columns doesn't show up
:param verbose: a boolean; if True, tells you when a dataframe is missing a column
:return: a list of the form [(index of column),(list of unique labels including index of that label, e.g.
['Appreciation','Appreciation[1]','Appreciation[2]'])
"""
newdict = {}
for entry in range(len(correspondences)):
if correspondences[entry][0] in dataframe.columns:
searchedlist = dataframe[correspondences[entry][0]].tolist()
splitlist = [i.split('|') for i in searchedlist]
foundlist = []
for e in splitlist: # each element in splitlist is currently a list
for i in e: # so i is a string
foundlist.append(i) # so now foundlist is a list of strings
foundlist = set(foundlist) # convert to set so we have uniques only
foundlist = [label for label in foundlist] # convert foundlist back to a list
newdict[correspondences[entry][0]] = foundlist
else:
if verbose:
print(dfname, "does not include column", correspondences[entry][0])
return newdict
def lookup_label(dataframe, column, label, commentid="dataframe", not_applicable=None, verbose=False,
clean_suffix='_cleaned.tsv'):
"""
Looks in the dataframe for rows matching the label and returns them.
:param dataframe: A pandas dataframe
:param column: which column in the dataframe to look in for the labels
:param label: which label to look for in the column
:param commentid: the name of the comment; the new row will have this as its first entry
:param bothids: whether to include both comment names (e.g. aboriginal_1 and source_xx_xx)
:param not_applicable: what to put in a cell if there is no data (e.g. something un-annotated)
:param verbose: whether to tell you when it's done
:param clean_suffix: the suffix appended to clean files. Default assumes you cleaned them with clean_comments.py
:return: a list that can be used as a new row or rows. If the label has no index (e.g. 'Appreciation' or '_'), then
all rows with those labels will be returned. If it has an index (e.g. 'Appreciation[3]'), then one row
representing that annotated span will be returned.
The fields in the list are, by column:
- the comment ID
- which sentence the span starts in
- which sentence it ends in
- which character it starts on
- which character it ends on
- which words are in the span
- the Attitude label for the span
- the Attitude polarity for the span
- the graduation label for the span
- the graduation polarity for the span
"""
# determine if we're looking at attitude, graduation, or negation
if 'att' in column:
layer = 'att'
elif 'gra' in column:
layer = 'gra'
elif column == 'negation':
layer = 'neg'
else:
layer = 'unknown'
# Check that both label and polarity columns are present
if ('attlab' in dataframe.columns) ^ ('attpol' in dataframe.columns):
if 'attlab' in dataframe.columns:
print(commentid, 'has attlab column but no attpol column')
if 'attpol' in dataframe.columns:
print(commentid, 'has attpol column but no attlab column')
if ('gralab' in dataframe.columns) ^ ('grapol' in dataframe.columns):
if 'gralab' in dataframe.columns:
print(commentid, 'has gralab column but no grapol column')
if 'grapol' in dataframe.columns:
print(commentid, 'has grapol column but no gralab column')
# look for labels with brackets (e.g. 'Appreciation[3]')
if '[' in label:
mask = [(label in i) for i in dataframe[column].tolist()]
founddf = dataframe[mask]
# get the sentence(s) of the label
foundsentstart = int(re.search(r'^.*-', founddf['sentpos'].tolist()[0]).group()[:-1])
foundsentend = int(re.search(r'^.*-', founddf['sentpos'].tolist()[-1]).group()[:-1])
# get the character positions for the new row
# look at which character the label starts in
foundcharstart = int(re.search(r'^.*-', founddf['charpos'].tolist()[0]).group()[:-1])
# look at which character the label ends in
foundcharend = int(re.search(r'-.*$', founddf['charpos'].tolist()[-1]).group()[1:])
# concatenate the words for the new row
foundwords = ''
for word in founddf['word']:
foundwords = foundwords + word + ' '
foundwords = foundwords[:-1]
# get the labels for the new row
# in case of pipes, figure out which one is the real label
posslabels = founddf[column].tolist()
posslabels = posslabels[0]
posslabels = posslabels.split('|')
labelindex = posslabels.index(label)
# now look through the columns and find the appropriate labels
# Each column is converted to a list. The first item in the list is used to find the label.
# This item is split by '|' in case of stacked annotations.
# Before, we found the index of the label we want. We get the found label from this index.
if layer == 'att':
if 'attlab' in founddf.columns:
foundattlab = founddf['attlab'].tolist()[0].split('|')[labelindex]
# We want to cut off the index (e.g. 'Appreciation[3]' -> 'Appreciation')
# search() finds everything up to the '[', and .group()[:-1] returns what it found, minus the '['
foundattlab = re.search(r'^.*\[', foundattlab).group()[:-1]
else:
foundattlab = not_applicable
if 'attpol' in founddf.columns:
foundattpol = founddf['attpol'].tolist()[0].split('|')[labelindex]
foundattpol = re.search(r'^.*\[', foundattpol).group()[:-1]
else:
foundattpol = not_applicable
foundgralab = not_applicable
foundgrapol = not_applicable
elif layer == 'gra':
if 'gralab' in founddf.columns:
foundgralab = founddf['gralab'].tolist()[0].split('|')[labelindex]
foundgralab = re.search(r'^.*\[', foundgralab).group()[:-1]
else:
foundgralab = not_applicable
if 'grapol' in founddf.columns:
foundgrapol = founddf['grapol'].tolist()[0].split('|')[labelindex]
foundgrapol = re.search(r'^.*\[', foundgrapol).group()[:-1]
else:
foundgrapol = not_applicable
foundattlab = not_applicable
foundattpol = not_applicable
elif layer == 'neg':
if 'negation' in founddf.columns:
foundneglab = founddf['negation'].tolist()[0].split('|')[labelindex]
foundneglab = re.search(r'^.*\[', foundneglab).group()[:-1]
else:
foundneglab = not_applicable
else:
print(label, "I can't tell which label this is.")
# put all that together into a list for a new row
if layer == 'att' or layer == 'gra':
foundrow = [commentid, foundsentstart, foundsentend, foundcharstart, foundcharend,
foundwords, foundattlab, foundattpol, foundgralab, foundgrapol]
elif layer == 'neg':
foundrow = [commentid, foundsentstart, foundsentend, foundcharstart, foundcharend,
foundwords, foundneglab]
else:
print("I couldn't make a new row because I don't know which label this is")
if verbose:
print('Done with comment', commentid, "label", label)
return foundrow
# look for unlabelled spans (i.e. label '_')
elif label == '_':
if layer == 'neg':
mask = [(label in i) for i in dataframe[column].tolist()]
founddf = dataframe[mask]
# If the layer is Attitude or Graduation, check for spans with a label but no polarity or vice versa
# and be sure that any spans returned as unlabelled have no label or polarity
elif layer == 'att' or layer == 'gra':
attmask = []
gramask = []
if 'attlab' in dataframe.columns and 'attpol' in dataframe.columns:
mask1 = [(label in i) for i in dataframe['attlab'].tolist()]
mask2 = [(label in i) for i in dataframe['attpol'].tolist()]
for i in range(len(mask1)):
if mask1[i] is not mask2[i]:
print('row', i, 'has mismatched Attitude labels')
attmask = [a and b for a, b in zip(mask1, mask2)]
if 'gralab' in dataframe.columns and 'grapol' in dataframe.columns:
mask3 = [(label in i) for i in dataframe['gralab'].tolist()]
mask4 = [(label in i) for i in dataframe['grapol'].tolist()]
for i in range(len(mask3)):
if mask3[i] is not mask4[i]:
print('row', i, 'has mismatched Graduation labels')
gramask = [a and b for a, b in zip(mask3, mask4)]
if attmask and not gramask:
mask = attmask
elif gramask and not attmask:
mask = gramask
elif attmask and gramask:
mask = [a and b for a, b in zip(attmask, gramask)]
elif not attmask and not gramask: # this will return all rows if there's no attlab or
mask = [True for i in range(len(dataframe))] # gralab, since there's no annotations at all.
founddf = dataframe[mask]
else:
print("Layer unrecognized when looking for unlabelled spans")
# find the sentences
sentences = []
for i in range(len(founddf['sentpos'])):
sentences.append(
int( # we want to do math on this later
re.search(
r'^.*-', founddf['sentpos'].tolist()[i] # finds whatever comes before a '-'
).group()[:-1] # returns the string it found
))
# find the character positions
charpositions = []
for i in range(len(founddf['charpos'])):
charpositions.append(
(int(re.search(r'^.*-', founddf['charpos'].tolist()[i]).group()[:-1]),
int(re.search(r'-.*$', founddf['charpos'].tolist()[i]).group()[1:]))
)
# find all the words
allfoundwords = founddf['word'].tolist()
# find consecutive unlabelled words
foundspans = []
span_number = -1
last_match = False
for i in range(len(allfoundwords)):
if i - 1 in range(len(allfoundwords)): # if this isn't the first word
# check if this word came right after the last one
if sentences[i - 1] == sentences[i] and\
(charpositions[i - 1][-1] == (charpositions[i][0] - 1) or\
charpositions[i - 1][-1] == (charpositions[i][0])):
if not last_match: # if this is not a continuation of the previous span
span_number += 1 # keep track of the number we're on (index of foundspans)
# add the row for this span to foundspans
if layer == 'att' or layer == 'gra':
foundspans.append([commentid, # comment ID
sentences[i-1], # sentence start
sentences[i], # sentence end
charpositions[i-1][0], # character start
charpositions[i][-1], # character end
allfoundwords[i - 1] + ' ' + allfoundwords[i], # words
not_applicable, # Labels are all assumed to be absent.
not_applicable, # Per earlier code, it should tell you if that is
not_applicable, # not actually the case.
not_applicable, ])
elif layer == 'neg':
foundspans.append([commentid, # comment ID
sentences[i-1], # sentence start
sentences[i], # sentence end
charpositions[i-1][0], # character start
charpositions[i][-1], # character end
allfoundwords[i - 1] + ' ' + allfoundwords[i], # words
not_applicable])
last_match = True # record these two i's as contiguous
else: # (this word is a continuation of the previous span)
foundspans[span_number].pop(4) # remove the ending char position so we can replace it
oldwords = foundspans[span_number].pop(4) # remove the words from the span to replace it
foundspans[span_number].insert(4, charpositions[i][-1]) # add the last character of this word
foundspans[span_number].insert(5, oldwords + ' ' + allfoundwords[i]) # add the words together
else:
last_match = False # record these two i's as non-contiguous
# check if this is the first pair of words we're looking at
if i == 1: # i would equal 1 bc we skip i=0 (since we looked backwards)
# if i=1 and the first and second words are non-contiguous, we need to add
# the first word to foundspans.
if layer == 'att' or layer == 'gra':
foundspans.append([commentid, # comment ID
sentences[i-1], # sentence start
sentences[i-1], # sentence end
charpositions[i-1][0], # character start
charpositions[i-1][-1], # character end
allfoundwords[i-1], # word
not_applicable, # Labels are all assumed to be absent.
not_applicable, # Per earlier code, it should tell you if that is
not_applicable, # not actually the case.
not_applicable, ])
elif layer == 'neg':
foundspans.append([commentid, # comment ID
sentences[i-1], # sentence start
sentences[i-1], # sentence end
charpositions[i-1][0], # character start
charpositions[i-1][-1], # character end
allfoundwords[i-1], # word
not_applicable])
# look ahead to see if the next word is a continuation of this span:
if i + 1 in range(len(sentences)):
if sentences[i + 1] != sentences[i] and charpositions[i + 1][-1] != (charpositions[i][0] + 1):
span_number = span_number + 1 # if so, keep track of the index
if layer == 'att' or layer == 'gra':
foundspans.append([commentid, # comment ID
sentences[i], # sentence start
sentences[i], # sentence end
charpositions[i][0], # character start
charpositions[i][-1], # character end
allfoundwords[i], # word
not_applicable, # Labels are all assumed to be absent.
not_applicable, # Per earlier code, it should tell you if that is
not_applicable, # not actually the case.
not_applicable, ])
elif layer == 'neg':
foundspans.append([commentid, # comment ID
sentences[i], # sentence start
sentences[i], # sentence end
charpositions[i][0], # character start
charpositions[i][-1], # character end
allfoundwords[i], # word
not_applicable])
# else: the loop continues
else: # if there is no following word and this one isn't a continuation, it's its own word.
span_number = span_number + 1
if layer == 'att' or layer == 'gra':
foundspans.append([commentid, # comment ID
sentences[i], # sentence start
sentences[i], # sentence end
charpositions[i][0], # character start
charpositions[i][-1], # character end
allfoundwords[i], # word
not_applicable, # Labels are all assumed to be absent.
not_applicable, # Per earlier code, it should tell you if that is
not_applicable, # not actually the case.
not_applicable, ])
elif layer == 'neg':
foundspans.append([commentid, # comment ID
sentences[i], # sentence start
sentences[i], # sentence end
charpositions[i][0], # character start
charpositions[i][-1], # character end
allfoundwords[i], # word
not_applicable]) # no negation
if verbose:
print('Done with comment', commentid, "label", label)
return foundspans
# look for one-word annotated spans (e.g. 'Appreciation'
elif ((label in attlabs) or
(label in attpols) or
(label in gralabs) or
(label in grapols) or
(label in neglabs)):
# create subset dataframe - stricter than other conditions
mask = [(label == i) for i in dataframe[column].tolist()]
founddf = dataframe[mask]
# find the sentences
sentences = []
for i in range(len(founddf['sentpos'])):
sentences.append(
int( # we want to do math on this later
re.search(
r'^.*-', founddf['sentpos'].tolist()[i] # finds whatever comes before a '-'
).group()[:-1] # returns the string it found, minus 1 character from the end
))
# find the character positions
charpositions = []
for i in range(len(founddf['charpos'])):
charpositions.append(
(int(re.search(r'^.*-', founddf['charpos'].tolist()[i]).group()[:-1]),
int(re.search(r'-.*$', founddf['charpos'].tolist()[i]).group()[1:]))
)
# find the words
allfoundwords = founddf['word'].tolist()
# in case of pipes, figure out which one is the real label
posslabels = founddf[column].tolist()
posslabels = posslabels[0]
posslabels = posslabels.split('|')
labelindex = posslabels.index(label)
# now look through the columns and find the appropriate labels
# Each column is converted to a list. The first item in the list is used to find the label.
# This item is split by '|' in case of stacked annotations.
# Before, we found the index of the label we want. We get the found label from this index.
foundspans = []
for i in range(len(founddf)):
# since these are one word long, the starting and ending sentences are the same.
foundsentstart = sentences[i]
foundsentend = foundsentstart
# find the characters the word starts and ends with
foundcharstart = charpositions[i][0]
foundcharend = charpositions[i][1]
# find the word
foundwords = allfoundwords[i]
if layer == 'att':
if 'attlab' in founddf.columns:
foundattlab = founddf['attlab'].tolist()[0].split('|')[labelindex]
else:
foundattlab = not_applicable
if 'attpol' in founddf.columns:
foundattpol = founddf['attpol'].tolist()[0].split('|')[labelindex]
else:
foundattpol = not_applicable
foundgralab = not_applicable
foundgrapol = not_applicable
elif layer == 'gra':
if 'gralab' in founddf.columns:
foundgralab = founddf['gralab'].tolist()[0].split('|')[labelindex]
else:
foundgralab = not_applicable
if 'grapol' in founddf.columns:
foundgrapol = founddf['grapol'].tolist()[0].split('|')[labelindex]
else:
foundgrapol = not_applicable
foundattlab = not_applicable
foundattpol = not_applicable
elif layer == 'neg':
if 'negation' in founddf.columns:
foundneglab = founddf['negation'].tolist()[0].split('|')[labelindex]
else:
foundneglab = not_applicable
else:
print(label, "I can't tell which label this is.")
# put all that together into a list for a new row
if layer == 'att' or layer == 'gra':
foundrow = [commentid, foundsentstart, foundsentend, foundcharstart, foundcharend,
foundwords, foundattlab, foundattpol, foundgralab, foundgrapol]
elif layer == 'neg':
foundrow = [commentid, foundsentstart, foundsentend, foundcharstart, foundcharend,
foundwords, foundneglab]
else:
print("I couldn't make a new row because I don't know which label this is")
# add that row to foundspans
foundspans.append(foundrow)
if verbose:
print('Done with comment', commentid, "label", label)
return foundspans
else:
print('Your label was not recognized')
# you can try commands like:
"""
testdf1 = readprojfile(appraisal_projectdirs[3], 'app')
lookup_label(testdf1,'attlab','_', commentid='testdf1')
lookup_label(testdf1,'attlab','Judgment[4]', commentid='testdf1')
lookup_label(testdf1,'attlab','Appreciation', commentid='testdf1')
lookup_label(testdf1, 'gralab', 'Force', commentid='testdf1')
testdf2 = readprojfile(negation_projectdirs[3], 'neg')
lookup_label(testdf2, 'negation', 'NEG', commentid='testdf2')
lookup_label(testdf2, 'negation', 'SCOPE[2]', commentid='testdf2')
lookup_label(testdf2, 'negation', '_', commentid='testdf2')
"""
# this variable will be used in a moment; it's the same as collabels, keeping only the 'label' parts
# it's used so that we don't search polarity redundantly
appraisal_search_correspondences = (appraisal_collabels[0], appraisal_collabels[2])
# column names for new dataframes:
appraisal_newheads = ['comment',
'sentstart',
'sentend',
'charstart',
'charend',
'span',
'attlab',
'attpol',
'gralab',
'grapol']
negation_newheads = ['comment',
'sentstart',
'sentend',
'charstart',
'charend',
'span',
'label']
def simplify_dataframe(dataframe, project, commentid="Dataframe", not_applicable=None, bothids=True,
clean_suffix='_cleaned.tsv', verbose=()):
"""
Uses all the labels in correspondences to create a new dataframe organized by span rather than by word.
:param dataframe: the dataframe to search and re-create
:param project: 'neg' for a negation project, 'app' for an appraisal project
:param commentid: the name of the comment; the new row will have this as its first entry
:param not_applicable: what to put in a cell if there is no data (e.g. something un-annotated)
:param bothids: whether to add in a column with the other id (e.g. aboriginal_1 or source_01...)
:param clean_suffix: the suffix added to clean files (this will be removed from commentid to find the other id)
:param verbose: an iterable containing one or more of the following strings:
missingcol: reports whenever a comment lacks an annotation for one or more columns
label_done: reports when each label has been searched for (same as verbose for lookup_label)
comment_done: reports when the function has finished running
:return: a new dataframe with the same content as the one given in the first place, but reorganized by span
rather than by word
"""
# set verbosity
if 'missingcol' in verbose:
verbose_missingcol = True
else:
verbose_missingcol = False
# set newcols and correspondences
if project == "neg" or project.lower() == "negation":
newcols = negation_newheads
correspondences = negation_collabels
project = "neg"
elif project == "app" or project.lower() == "appraisal":
newcols = appraisal_newheads
correspondences = appraisal_search_correspondences
project = "app"
else:
print("Project type not recognized. Use 'neg' or 'app'.")
newcols = None
correspondences = None
# find the labels to look for
labinds = getlabinds_df(dataframe, correspondences=correspondences, dfname=commentid, verbose=verbose_missingcol)
# search the old dataframe and create a list to later add as rows to the empty one
if 'label_done' in verbose:
v_label_done = True
else:
v_label_done = False
foundrows = []
for i in range(len(correspondences)):
searchcolumn = correspondences[i][0] # which column to look in
if searchcolumn in dataframe.columns:
searchlabels = labinds[searchcolumn] # which labels to look for in that column
for searchlabel in searchlabels:
foundstuff = lookup_label(dataframe,
searchcolumn,
searchlabel,
commentid=commentid,
not_applicable=not_applicable,
verbose=v_label_done)
if '[' in searchlabel: # in this case, foundstuff is one row of data
foundrows.append(foundstuff)
else: # in this case, foundstuff is many rows of data
for row in foundstuff:
foundrows.append(row)
# if foundrows is empty, then instead of returning an empty df, return a df with a None-annotated row.
if not foundrows:
# find the sentences
sentences = []
# add the first sentence number to sentences
sentences.append(
int(
re.search(
r'^.*-', dataframe['sentpos'].tolist()[0] # finds whatever comes before a '-'
).group()[:-1] # returns the string it found
))
# add the last sentence number to sentences
sentences.append(
int(
re.search(
r'^.*-', dataframe['sentpos'].tolist()[-1] # finds whatever comes before a '-'
).group()[:-1] # returns the string it found
))
# find the character positions
charpositions = (int(re.search(r'^.*-', dataframe['charpos'].tolist()[0]).group()[:-1]),
int(re.search(r'-.*$', dataframe['charpos'].tolist()[-1]).group()[1:]))
# find all the words
allfoundwords = dataframe['word'].tolist()
allfoundwords = " ".join(allfoundwords)
if project == "app":
foundrows.append([commentid, # comment ID
sentences[0], # sentence start
sentences[1], # sentence end
charpositions[0], # character start
charpositions[1], # character end
allfoundwords, # word
not_applicable, # Labels are all assumed to be absent.
not_applicable,
not_applicable,
not_applicable, ])
elif project == "neg":
foundrows.append([commentid, # comment ID
sentences[0], # sentence start
sentences[1], # sentence end
charpositions[0], # character start
charpositions[1], # character end
allfoundwords, # word
not_applicable]) # no negation
# make the rows into a new df
newdf = pd.DataFrame(foundrows, columns=newcols)
# sort by which character the row starts with in ascending order, then which character it ends with descending
# this means that it will read chronologically, with longer spans appearing first
newdf = newdf.sort_values(by=['charstart', 'charend'], ascending=[True, False])
# lookup_label will return duplicate rows when there is a span annotated only for Attitude or only for Graduation.
newdf = newdf.drop_duplicates()
# now, if necessary, add a column for the other comment id
if bothids:
commentid = commentid[:-len(clean_suffix)] + '.txt'
if commentid in mappingdict1:
otherid = mappingdict1[commentid]
elif commentid in mappingdict2:
otherid = mappingdict2[commentid]
else:
otherid=''
print("No other comment id found for", commentid + '.')
if otherid:
newdf['comment_counter'] = otherid
if 'comment_done' in verbose:
print(commentid, "processed")
return newdf
# try simplify_dataframe(testdf1, appraisal_newheads, appraisal_search_correspondences, commentid="testdf1")
def combine_annotations(paths, project, not_applicable=None, bothids=True, clean_suffix='_cleaned.tsv', verbose=()):
"""
Takes cleaned WebAnno TSVs from given paths and reorganizes them into one single dataframe, with each row
representing a span (not a word, as original TSV rows do).
:param paths: where your cleaned WebAnno TSVs can be found
:param project: 'neg' for a negation project, 'app' for an appraisal project
:param not_applicable: what to put in a cell if there is no data (e.g. something un-annotated)
:param bothids: whether to include a column with the other form of identification (see simplify_dataframe())
:param clean_suffix: the suffix for cleaned files (this is removed if bothids is True, so that the mapping
dictionaries work.) (see simplify_dataframe()).
:param verbose: an iterable containing one or more of the following strings:
missingcol: reports whenever a comment lacks an annotation for one or more columns
label_done: reports when each label has been searched for (same as verbose for lookup_label)
comment_start: before processing a comment, reports which commentid it is about to process
comment_done: reports when the function has finished with each comment
all_done: reports when the function is finished running
:return: A new dataframe incorporating the information of all the TSVs in paths. Each row of the dataframe is one
span.
"""
# set newcols and correspondences
if project.lower() == "neg" or project.lower() == "negation":
newcols = negation_newheads
project = "neg"
elif project.lower() == "app" or project.lower() == "appraisal":
newcols = appraisal_newheads
project = "app"
else:
print("Project type not recognized. Use 'neg' or 'app'.")
newcols = None
newdf = pd.DataFrame(columns=newcols)
for path in paths:
commentid = path.split('/')[-1]
if 'comment_start' in verbose:
print("Processing comment", commentid)
originaldf = readprojfile(path, project)
founddf = simplify_dataframe(originaldf,
project,
commentid=commentid,
bothids=bothids,
clean_suffix=clean_suffix,
not_applicable=not_applicable,
verbose=verbose)
newdf = newdf.append(founddf)
if 'all_done' in verbose:
print("New dataframe created.")
return newdf
# try combine_annotations(testdirs, 'app')
if appraisal_projectpath:
if mapping_csv:
combined_appraisal_dataframe = combine_annotations(appraisal_projectdirs,
'app',
not_applicable='None', # a string works better for R
verbose=('comment_start',))
if appraisal_writepath:
combined_appraisal_dataframe.to_csv(appraisal_writepath)
print("Appraisal dataframe exported.")
else:
print("Not exporting Appraisal project as no path was specified.")
else:
combined_appraisal_dataframe = combine_annotations(appraisal_projectdirs,
'app',
not_applicable='None',
bothids=False,
verbose=('comment_start',))
if appraisal_writepath:
combined_appraisal_dataframe.to_csv(appraisal_writepath)
print("Appraisal dataframe exported.")
else:
print("Not exporting Appraisal project as no path was specified.")
else:
print("Not combining Appraisal project as no path was specified.")
if negation_projectpath:
combined_negation_dataframe = combine_annotations(negation_projectdirs,
'neg',
not_applicable='None',
verbose=('comment_start',))
if negation_writepath:
combined_negation_dataframe.to_csv(negation_writepath)
print("Negation dataframe exported.")
else:
print("Not exporting Negation project as no path was specified.")
else:
print("Not combining Negation project as no path was specified.")
| 41,827 | 51.285 | 119 | py |
SOCC | SOCC-master/scripts/projects_to_tsv.py | from smart_open import smart_open
import pandas as pd
import re
from io import StringIO
# find the comments
appraisal_comments_path = input('Path to combined Appraisal WebAnno formatted comments tsv'
'(e.g. C:\\...\\combined_appraisal_webanno.tsv): ')
negation_comments_path = input('Path to combined negation WebAnno formatted comments tsv'
'(e.g. C:\\...\\combined_negation_webanno.tsv): ')
mapping_csv = input("Path to your mapping of names e.g. 'C:\\...\\comment_counter_appraisal_mapping.csv'")
contox_path = input("Path to constructiveness and toxicity annotations e.g."
"'C:\\...\\SFU_constructiveness_toxicity_corpus.csv'")
writename = input('Name for the file that will be created (e.g. all_socc_annotations): ') + '.tsv'
writepath = input('Folder to write the new file to (e.g. C:\\...\\Documents\\): ') + writename
# what to put for blank entries:
blank_entry = '_'
# split the comments so that we can iterate over each individual one
if appraisal_comments_path:
with smart_open(appraisal_comments_path, 'r') as f:
comments_str = f.read()
appraisal_comments_list = comments_str.split('#end of comment\n\n')
else:
print("Not using Appraisal annotations as no path was provided.")
if negation_comments_path:
with smart_open(negation_comments_path, 'r') as f:
comments_str = f.read()
negation_comments_list = comments_str.split('#end of comment\n\n')
else:
print("Not using negation annotations as no path was provided.")
# these are the actual column headers for the TSV files
# some Appraisal TSVs do not have graduation, hence the need for two lists of names
appraisal_longheaders = ['sentpos', 'charpos', 'word', 'attlab', 'attpol', 'gralab', 'grapol']
appraisal_shortheaders = ['sentpos', 'charpos', 'word', 'attlab', 'attpol']
negation_shortheaders = ['sentpos', 'charpos', 'word', 'negation']
negation_longheaders = ['sentpos', 'charpos', 'word', 'negation', 'XNEG']
# some comments have no annotations
no_annotation = ['sentpos', 'charpos', 'word']
appraisal_possnames = [no_annotation, appraisal_shortheaders, appraisal_longheaders]
negation_possnames = [no_annotation, negation_shortheaders, negation_longheaders]
# labels that can be found in different columns
attlabs = ('Appreciation', 'Affect', 'Judgment')
attpols = ('pos', 'neu', 'neg')
gralabs = ('Force', 'Focus')
grapols = ('up', 'down')
neglabs = ('NEG', 'SCOPE', 'FOCUS', 'XSCOPE')
# create some tuples to show which columns go with which labels
# doesn't include polarity because we'll pull that out based on the label
# (each instance of Attitude should have both label and polarity)
appraisal_collabels = ((appraisal_longheaders[3], attlabs),
(appraisal_longheaders[5], gralabs),)
# this next tuple is within another tuple so that the same commands we need later will iterate correctly
negation_collabels = (('negation', neglabs),)
# more info to help process the TSVs
sentence_indicator = '#Text=' # what WebAnno TSVs have written to indicate the full text of a sentence
sent_startpos = len(sentence_indicator) # where the text for a sentence would actually start
name_indicator = '#comment: ' # text added to the combined tsv to indicate the name of each comment
name_startpos = len(name_indicator) # where the text for the comment name would actually start
def readprojfile_withblanks(source, project,
neg_possnames=negation_possnames, app_possnames=appraisal_possnames,
not_applicable=blank_entry):
"""
Reads a WebAnno TSV into a pandas dataframe. One column is often read as full of NaN's due to the TSVs'
original formatting, so this function drops any columns with NaN's. This function also adds blanks for any columns
which are not included in the original TSV.
:param source: the path to a WebAnno TSV
:param possnames: the headers that may occur in the TSV, as a list of lists of headers.
The function will check each list within possnames to see if its length is equal to the number of columns
:param project: 'app' if Appraisal, 'neg' if negation.
:param neg_possnames: a list of lists of possible headers for the negation TSVs
:param app_possnames: a list of lists of possible headers for the Appraisal TSVs
:param not_applicable: what to put for entries that are empty
:return: a pandas dataframe containing the information in the original TSV
"""
# set possnames
if project == "neg" or project.lower() == "negation":
possnames = neg_possnames
project = "neg"
elif project == "app" or project.lower() == "appraisal":
possnames = app_possnames
project = "app"
else:
print("Project type not recognized. Use 'neg' or 'att'.")
possnames = None
newdf = pd.read_csv(source, sep='\t', header=None)
newdf = newdf.dropna(axis=1, how='all')
for headers in possnames:
if len(newdf.columns) == len(headers):
newdf.columns = headers
if all([len(newdf.columns) != i for i in [len(headers) for headers in possnames]]):
print("No correct number of columns in", source)
# add in missing columns
if len(newdf.columns) != len(possnames[-1]):
for column in possnames[-1]:
if column not in newdf.columns:
newdf[column] = not_applicable
return newdf
# We will want the names to match accross annotations, so let's get the mapping in
if mapping_csv:
mapping1 = pd.read_csv(mapping_csv)
list1 = mapping1['appraisal_negation_annotation_file_name'].tolist()
list2 = mapping1['comment_counter'].tolist()
# dictionary of original to comment counter names
mappingdict1 = {}
for i in range(max(len(list1), len(list2))):
mappingdict1[list1[i]] = list2[i]
# same dictionary in reverse
mappingdict2 = {}
for i in range(max(len(list1), len(list2))):
mappingdict2[list2[i]] = list1[i]
# go through the comments, extract the sentences, clean the comments so pandas can read them later, then build a
# pandas data frame combining each comment as it is done
if appraisal_comments_path and negation_comments_path:
# Prepare a combined df
appdf = pd.DataFrame()
combined_sentences = pd.DataFrame(columns=('comment', 'sent'))
# Do stuff with appraisal
print('Processing appraisal comments')
for comment in appraisal_comments_list:
if comment: # due to how the comments list is made, it ends in a '' which can't be read properly
# extract sentences and clean them
linelist = comment.split('\n')
clean_comment_lines = []
sentences = []
lastmatch_sent = False # whether the last line had the sentence text in it
for line in linelist:
# get the sentences and comment names and discard other commented lines
if re.match('#', line): # if WebAnno commented out the line
if re.match(sentence_indicator, line): # if the line has the text for a sentence
# add that sentence to the sentences list
if not lastmatch_sent:
# Normally, each line with '#Text=' is a new sentence. Add the relevant part to the list:
sentences.append(line[sent_startpos:])
else:
# for some reason aboriginal_11 has 2 '#Text=' lines, but only one sentence.
# This conditional addresses that
sentences[-1] = sentences[-1] + line[sent_startpos:]
lastmatch_sent = True
else:
lastmatch_sent = False
if re.match(name_indicator, line): # if the line has the text for a comment name
# set the name
# file extensions may vary between .txt and .tsv because of how files were managed in WebAnno.
# The extension is replaced so that names match across projects.
oldname = line[name_startpos:]
oldname = oldname[:-4] + '.txt'
# if the line was not commented out, put it in the new "clean" list:
else:
clean_comment_lines.append(line)
lastmatch_sent = False
# Now that we have the sentences and comment name, get them into the combined sentences df
# one file was misnamed in an earlier version of the Appraisal files. Let's fix that here:
if oldname == 'aboriginal_16.txt':
oldname = 'aboriginal_17.txt'
# let's also add the comment counter name
comment_counter = mappingdict1[oldname]
sentences_df = pd.DataFrame()
sentences_df['sent'] = sentences
sentences_df['oldname'] = oldname
sentences_df['comment_counter'] = comment_counter
combined_sentences = combined_sentences.append(sentences_df)
print('Processing comment', str(appraisal_comments_list.index(comment)) + ':', oldname)
# put the comment into a pandas df
clean_comment = '\n'.join(clean_comment_lines)
clean_comment_buffer = StringIO(clean_comment)
clean_df = readprojfile_withblanks(clean_comment_buffer, 'app')
clean_df['oldname'] = oldname
clean_df['comment_counter'] = comment_counter
appdf = appdf.append(clean_df)
# get the negation columns
negdf = pd.DataFrame()
for comment in negation_comments_list:
if comment: # due to how the comments list is made, it ends in a '' which can't be read properly
# extract sentences and clean them
linelist = comment.split('\n')
clean_comment_lines = []
# sentences list not needed since we have this from Appraisal
for line in linelist:
# get the sentences and comment names and discard other commented lines
if re.match('#', line): # if WebAnno commented out the line
if re.match(name_indicator, line): # if the line has the text for a comment name
# set the name
oldname = line[name_startpos:]
# file extensions may vary between .txt and .tsv because of how files were managed in WebAnno.
# The extension is replaced so that names match across projects.
oldname = oldname[:-4] + '.txt'
# if the line was not commented out, put it in the new "clean" list:
else:
clean_comment_lines.append(line)
print('Processing comment', str(negation_comments_list.index(comment)) + ':', oldname)
# let's also add the comment counter name
comment_counter = mappingdict1[oldname]
# put the comment into a pandas df
clean_comment = '\n'.join(clean_comment_lines)
clean_comment_buffer = StringIO(clean_comment)
clean_df = readprojfile_withblanks(clean_comment_buffer, 'neg')
clean_df['oldname'] = oldname
clean_df['comment_counter'] = comment_counter
negdf = negdf.append(clean_df)
# Combine appraisal and negation annotations
app_commentlist = set(appdf['oldname'].tolist()) # list of unique comment names for App
neg_commentlist = set(negdf['oldname'].tolist()) # and for neg
# check that the same comments are in both
if neg_commentlist != app_commentlist:
if not all([i in neg_commentlist for i in app_commentlist]):
for comment in neg_commentlist:
if comment not in app_commentlist:
print(comment, 'is in negation but not Appraisal')
if not all([i in app_commentlist for i in neg_commentlist]):
for comment in app_commentlist:
if comment not in neg_commentlist:
print(comment, 'is in Appraisal but not negation')
combined_df = appdf
combined_df['negation'] = blank_entry
combined_df['XNEG'] = blank_entry
for comment in app_commentlist:
print('Combining Appraisal and negation annotations for', comment)
combined_df.loc[combined_df.oldname == oldname, 'negation'] = negdf.loc[negdf.oldname == oldname, 'negation']
combined_df.loc[combined_df.oldname == oldname, 'XNEG'] = negdf.loc[negdf.oldname == oldname, 'XNEG']
# Separate the sentence numbers in combined_df
sentence_numbers = []
for i in combined_df['sentpos'].tolist():
# find the number before a hyphen, return that number, then coerce it into an integer and add it to
# sentence_numbers
sentence_numbers.append(int(re.search(r'^.*-', i).group()[:-1]))
combined_df['sentence'] = sentence_numbers
# Read the constructiveness and toxicity df
if contox_path:
contox_columns = ('comment', 'is_constructive', 'is_constructive:confidence',
'toxicity_level', 'toxicity_level:confidence')
contox_df = pd.read_csv(contox_path)
# Sort the comment list so that the TSV is more easily navigable
comment_counter_list = [mappingdict1[i] for i in app_commentlist]
comment_counter_list.sort()
# Put all the information together
newstring = ''
for comment in comment_counter_list:
print('Adding constructiveness and toxicity to', comment)
comment_slice = combined_df.loc[combined_df.comment_counter == comment]
contox_slice = contox_df.loc[contox_df.comment_counter == comment]
# .tolist()[0] is added to get a straightforward string out of the relevant entry
comment_oldname = comment_slice['oldname'].tolist()[0]
comment_is_constructive = contox_slice['is_constructive'].tolist()[0]
comment_is_const_conf = contox_slice['is_constructive:confidence'].tolist()[0]
comment_toxicity = contox_slice['toxicity_level'].tolist()[0]
comment_tox_conf = contox_slice['toxicity_level:confidence'].tolist()[0]
# Put the data describing the whole comment into one string
# \n is replaced with | to make things more human-readable
comment_prefix = '# comment= ' + comment_oldname + ' / ' + comment + '\n' + \
'# is_constructive= ' + comment_is_constructive.replace('\n', '|') + '\tconfidence= ' + \
str(comment_is_const_conf).replace('\n', '|') + '\n' + \
'# toxicity_level= ' + comment_toxicity.replace('\n', '|') + '\tconfidence= ' + \
str(comment_tox_conf).replace('\n', '|')
# get the sentences for this comment
sentences_slice = combined_sentences.loc[combined_sentences.comment_counter == comment]
sentences = sentences_slice['sent'].tolist()
# go through each sentence, get a slice for the rows of that sentence, put a header containing the sentence,
# then put the annotated words after that
comment_body = ''
for i in set(comment_slice['sentence'].tolist()):
small_slice = comment_slice.loc[comment_slice.sentence == i]
small_slice = small_slice.drop(columns=['oldname', 'comment_counter', 'sentence'])
comment_body = comment_body + '\n\n#text = ' + sentences[i-1] + '\n' + \
small_slice.to_string(index=False)
# (i-1 because WebAnno starts counting at 1 but Python starts at 0)
newstring = newstring + comment_prefix + comment_body + '\n\n\n'
if writepath:
with smart_open(writepath, 'w') as f:
print('Writing new file...')
f.write(newstring)
print('New file written!')
else:
print('No write path was given, so no file will be generated.')
else:
print('No constructiveness/toxicity path was given, so this information won\'t show up')
else:
print('Either Appraisal or negation path was missing. What can I do?')
| 16,422 | 54.296296 | 120 | py |
robogym | robogym-master/setup.py | #!/usr/bin/env python3
from setuptools import find_packages, setup
def setup_robogym():
setup(
name="robogym",
version=open("ROBOGYM_VERSION").read(),
packages=find_packages(),
install_requires=[
# Fixed versions
"click==7.0",
"collision==1.2.2",
"gym==0.15.3",
"kociemba==1.2.1",
"mujoco-py==2.0.2.13",
"pycuber==0.2.2",
"matplotlib==3.1.2",
"transforms3d==0.3.1",
# Minimum versions
"jsonnet>=0.14.0",
"pytest>=4.6.9",
"scikit-learn>=0.21.3",
"trimesh>=3.5.23",
"mock>=4.0.2",
],
python_requires=">=3.7.4",
description="OpenAI Robogym Robotics Environments",
include_package_data=True,
)
setup_robogym()
| 862 | 24.382353 | 59 | py |
robogym | robogym-master/robogym/robot_exception.py | """Module with custom exception code for robots."""
class RobotException(Exception):
"""Base class for custom exceptions relative to or raised by robots."""
pass
| 173 | 20.75 | 75 | py |
robogym | robogym-master/robogym/robot_env.py | import abc
import logging
import random
import time
from collections import OrderedDict
from copy import deepcopy
from typing import Any, Callable, Dict, Generic, List, Optional, Tuple, Type, TypeVar
import attr
import gym
import gym.spaces as spaces
import numpy as np
from robogym.goal.goal_generator import GoalGenerator
from robogym.mujoco.modifiers.base import Modifier
from robogym.mujoco.simulation_interface import SimulationInterface
from robogym.mujoco.warning_buffer import MjWarningBuffer
from robogym.observation.common import Observation, ObservationProvider, SyncType
from robogym.randomization.action import ActionRandomizer
from robogym.randomization.env import (
EnvActionRandomizer,
EnvObservationRandomizer,
EnvParameterRandomizer,
EnvRandomization,
EnvSimulationRandomizer,
)
from robogym.randomization.observation import ObservationRandomizer
from robogym.randomization.sim import SimulationRandomizer
from robogym.robot.robot_interface import Robot
from robogym.robot_exception import RobotException
from robogym.utils.env_utils import gym_space_from_arrays
from robogym.utils.multi_goal_tracker import MultiGoalTracker
logger = logging.getLogger(__name__)
T = TypeVar("T")
def build_nested_attr(t: Callable[..., T], default: Optional[Dict] = None) -> T:
"""
Build attr.attrib for given user defined attr type which can be nested into
another attr.attrib class. This is to overcome that issue that nested dict
won't be automatically deserialized into nested attrib class. For example
directly define the following attrib hierarchy
@attrs.s()
class B:
name: str
@attrs.s()
class A:
b: B
and create a instance via A(b={'name': 'foo'}) doesn't work because {'name': 'foo'}
won't be deserialized into class B. If you create a nested attrib using this function
as
@attrs.s()
class A:
b: build_nested_attr(B)
The nested serialization will work correctly.
"""
if default is None:
default = {}
def converter(val):
new_val = default.copy()
if isinstance(val, t):
return val
else:
assert isinstance(val, dict)
new_val.update(val)
return t(**new_val)
return attr.ib(converter=converter, default=t(**default))
def get_generic_param_type(type_: Type, i: int, expected_type: Type):
"""
Get i-th generic type parameter for given class. For example
if type_ extends Generic[A, B, C] then
get_generic_param_type(type_, 0) -> A
get_generic_param_type(type_, 1) -> B
An error will be raised if the generic type is not a subclass of
expected type.
"""
type_param = type_.__orig_bases__[0].__args__[i]
if isinstance(type_param, TypeVar): # type: ignore
type_param = type_param.__bound__
assert issubclass(type_param, expected_type), (
f"Parameter class {type_param} is not a subclass of {expected_type}"
f"Please make sure type arguments for {type_} is correctly specified."
)
return type_param
@attr.s(auto_attribs=True)
class RobotEnvParameters:
# How many steps with random action do we take when the environment is initialized.
# This should be nonzero for sim2real training configs.
n_random_initial_steps: int = 10
@attr.s(auto_attribs=True)
class RobotEnvConstants:
""" Parameters of the env - set once and for all """
# How many mujoco warnings we store max per episode
mujoco_warning_capacity: int = 5
# Whether policy actions represent target position of the fingers or change in positions of
# the fingers
relative_action: bool = True
# Number of bins for discrete action.
n_action_bins: int = 11
# If this environment is used by physical robot.
physical: bool = False
#####################
# Curriculum settings
# Number of mujoco simulation steps per environment step
mujoco_substeps: int = 10
# timestep for each mujoco simulation step.
mujoco_timestep: float = 0.002
####################
# Observation related constants.
# All enabled observation providers. Note that mujoco and goal doesn't
# need to be specified here because they are assumed to exist for all
# environments.
observation_providers: list = []
# Map between observation key to observation provider. This can used to
# override predefined default_observation_map to support different
# provider for each observation.
observation_configs: dict = {}
#####################
# Wrapper settings.
# Below are constants for commonly used wrappers e.g. recording wrapper.
# If randomize the environment.
randomize: bool = True
#####################
# Multiple successes settings
# Threshold for success conditions
success_threshold: dict = {}
# How many successes needed to stop the episode.
successes_needed: int = 5
# Reward for a single success if all the distances are within the `success_threshold`
success_reward: float = 5.0
# Number of seconds to sample the amount of time (in seconds) that success needs to
# stay in successful state to be counted. This defines the range (in seconds) and the
# actual pause time is sampled uniformly from within this range.
success_pause_range_s: Tuple[float, float] = (0.0, 0.0)
# Max timesteps for each goal until timeout.
max_timesteps_per_goal: Optional[int] = None
# If true, check if goal is reachable from current state after each step.
check_goal_reachable: bool = False
# How many gym steps we can make before considering the goal is unreachable.
max_steps_goal_unreachable: int = 10
# If yes, include `goal_distance_reward` into the final env reward.
# How `goal_distance_reward` is calculated depends on the specific env.
use_goal_distance_reward: bool = True
#####################
# Randomizer settings
# Path for all enabled randomizers. Randomizer path is defined as
# dot separated list of randomizer names along the chain e.g.
# parameters # Top level parameters randomizer
# observation.observation_delay # Observation delay randomizer
# under top level observation randomizer
randomizers: List[str] = []
class ObservationMapValue(Dict[str, Type[Observation]]):
"""
Value type for observation map. Example ways to instantiate
this type are:
ObservationMapValue({'mujoco': 'MujocoCubePosObservation'})
ObservationMapValue({
'mujoco': MujocoCubePosObservation,
'phasespace': PhasespaceCubePosObservation,
}, default='phasespace')
If there is only one provider available for given observation, default
doesn't need to be explicitly specified.
default provider will be used if no provider is explicitly specified for
this observation key in RobotEnvConstants.observation_configs.
"""
def __init__(
self,
providers_to_obs_class: Dict[str, Type[Observation]],
default: Optional[str] = None,
):
"""
:param providers_to_obs_class: Map between provider name to observation class.
:param default: Name of default observation provider.
"""
super().__init__(**providers_to_obs_class)
self._default = default
def update(self, target: "ObservationMapValue", **kwargs): # type: ignore
super().update(target, **kwargs)
self._default = target._default or self._default
def get_default(self):
if self._default is None:
assert len(self) == 1, (
f"There are multiple providers available for this observation: "
f"{self.keys()}, but no default."
)
return list(self.keys())[0]
else:
assert self._default in self, f"Invalid default provider {self._default}"
return self._default
class RobotEnvObserver:
"""
Class to encapsulate observation related logic.
"""
def __init__(
self,
mujoco_simulation: SimulationInterface,
providers: Dict[str, ObservationProvider],
observations: Dict[str, Observation],
):
self.mujoco_simulation = mujoco_simulation
self.providers = providers
self.observations = observations
self._observation_space = None
self.reset()
def reset(self):
"""
Reset providers and observations.
"""
for provider in self.providers.values():
provider.reset()
for observation in self.observations.values():
observation.reset()
def sync(self, sync_type=SyncType.STEP):
"""
Sync all observation providers. Ideally this should only be called once every step.
"""
# Sync observation providers to make sure they return latest
# information.
for provider in self.providers.values():
if provider.SYNC_TYPE.value <= sync_type.value:
provider.sync()
def observe(self) -> dict:
"""
Return read only view of current observation of the environment. Calling this method
multiple times in a row should yield same result without any side effect.
"""
return OrderedDict((key, o.get()) for key, o in self.observations.items())
def record_data(self) -> dict:
"""
Return data for recording wrapper.
"""
data = {}
for provider in self.providers.values():
data.update(provider.record_data())
for obs in self.observations.values():
data.update(obs.record_data())
return data
class EnvMeta(abc.ABCMeta):
"""
Metaclass for the environment to properly initialize environment after "basic" init has
been done.
This can ensure initialize() is called after __init__ of this class and all subclasses have
finished. To follow the pattern please make sure:
- Put logic in __init__ if it should be called before __init__of all subclasses are called.
- Put logic in initialize if it should be called after __init__ of all subclasses are called.
"""
def __call__(cls, *args, **kwargs):
"""Called when you call MyNewClass() """
obj = type.__call__(cls, *args, **kwargs)
obj.initialize()
return obj
PType = TypeVar("PType", bound=RobotEnvParameters)
CType = TypeVar("CType", bound=RobotEnvConstants)
SType = TypeVar("SType", bound=SimulationInterface)
class RobotEnv(gym.Env, Generic[PType, CType, SType], metaclass=EnvMeta):
"""
Base class for robot environments.
"""
metadata = {
"render.modes": ["human", "rgb_array"],
}
def __init__(
self,
parameters: PType,
constants: CType,
mujoco_simulation: SType,
mujoco_modifiers: Dict[str, Modifier],
robot: Robot,
goal_generation: GoalGenerator,
randomization: EnvRandomization,
starting_seed: Optional[int] = None,
):
self.parameters = parameters
self.constants = constants
self.mujoco_simulation = mujoco_simulation
self._cached_robot = robot
self.randomization = randomization
self.latest_action_metadata: Dict[str, object] = {}
# Container for potential mujoco errors/warnings
self.warning_buffer = MjWarningBuffer(
maxlen=self.constants.mujoco_warning_capacity
)
self.warning_buffer.enter()
# Random seed state
self._last_seed = (
starting_seed
if starting_seed is not None
else random.randint(0, 2 ** 32 - 1)
)
self._random_state = np.random.RandomState(self._last_seed)
# Episode step counter
self.t = 0
# "Goal" information
self._previous_goal_distance = None
self._goal = None # type: ignore
self._goal_info_cache = None
self.goal_generation = goal_generation
# Environment observation/action spaces
self.action_space = spaces.Box(
low=-1.0, high=1.0, shape=(len(robot.zero_control()),), dtype=np.float32
)
self.action_space.seed(self._last_seed)
# Will be initialized later
self.observer: Optional[RobotEnvObserver] = None
self._observation_space = None
# List of mujoco modifiers
self.modifiers: List[Tuple[str, Any]] = []
self.reward_names = ["env", "goal", "success"]
self.last_update_time = time.time()
self.mujoco_simulation.mj_sim.render_callback = self._render_callback
for parameter_name, modifier in mujoco_modifiers.items():
self.register_modifier(parameter_name, modifier)
# Set up trackers for multi-successes goals
self.multi_goal_tracker = MultiGoalTracker(
mujoco_simulation=self.mujoco_simulation,
reset_goal_generation_fn=self.reset_goal_generation,
reset_goal_fn=self.reset_goal,
max_timesteps_per_goal=self.constants.max_timesteps_per_goal,
success_reward=self.constants.success_reward,
successes_needed=self.constants.successes_needed,
success_pause_range_s=self.constants.success_pause_range_s,
max_steps_goal_unreachable=self.constants.max_steps_goal_unreachable,
check_goal_reachable=self.constants.check_goal_reachable,
use_goal_distance_reward=self.constants.use_goal_distance_reward,
goal_types=self.goal_generation.goal_types(),
random_state=self._random_state,
)
def initialize(self):
"""
Initialization to be ran after __init__ of this class and all the subclasses is finished
"""
self._setup_simulation_from_parameters()
if "orrb" in self.constants.observation_providers:
self._reset()
self._goal = self._next_goal()
self.update_goal_info()
self.observer = self._build_observer()
###############################################################################################
# Initialize observation related stuff.
def _build_observer(self):
"""
Initialize observation providers for the environment.
"""
providers = self._build_observation_providers()
observations = self._build_observations(providers)
return RobotEnvObserver(self.mujoco_simulation, providers, observations)
def _build_observations(self, providers: Dict[str, ObservationProvider]):
observation_map = self._default_observation_map()
observations: Dict[str, Observation] = {}
for obs_key, obs_map in observation_map.items():
provider_name = self.constants.observation_configs.get(
obs_key, obs_map.get_default()
)
assert provider_name in providers, (
f"Observation {obs_key} is configured to use provider {provider_name} "
f"which is not enabled. Enabled providers are: "
f"{providers.keys()}"
)
obs_class = obs_map[provider_name]
provider = providers[provider_name]
observations[obs_key] = obs_class(provider)
return observations
###############################################################################################
# Internal API - must be overridden - observation
@abc.abstractmethod
def _build_observation_providers(self) -> Dict[str, ObservationProvider]:
"""
Build all observation providers for this environment.
"""
pass
@abc.abstractmethod
def _default_observation_map(self) -> Dict[str, ObservationMapValue]:
"""
Return map between observation key and map between observation provider
and observation class. This map should only contain simulation based observations.
See implementation of other environments for example.
"""
pass
###############################################################################################
# Internal API - may be overridden - mujoco simulation interface
def _get_simulation_reward_with_done(self, info: dict) -> Tuple[float, bool]:
""" Return current reward and whether episode is finished
:param info: Current info dict for this env step.
"""
return 0.0, False
def _get_simulation_info(self) -> dict:
""" Return extra information about the environment """
# Just a stub for now
return {}
def _set_action(self, action):
""" Set action for the hand"""
action = np.asarray(action)
action = np.clip(action, self.action_space.low, self.action_space.high)
ctrl = self.robot.denormalize_position_control(
position_control=action, relative_action=self.constants.relative_action,
)
self.robot.set_position_control(ctrl)
###############################################################################################
# Internal API - To be exposed to the child classes and wrappers
def register_modifier(self, parameter_name: str, modifier_object: Modifier):
""" Register given modifier for a given parameter value """
modifier_object.initialize(self.sim)
self.modifiers.append((parameter_name, modifier_object))
def _setup_simulation_from_parameters(self):
"""
Set all the simulation parameters from the current settings.
You may override it or just leave it as it is for a very basic setup.
"""
for param_name, modifier in self.modifiers:
modifier(getattr(self.parameters, param_name))
def _render_callback(self, _sim, _viewer):
"""A custom callback that is called before rendering. Can be used
to implement custom visualizations.
"""
pass
def _reset(self):
"""
Custom reset logic that can be overloaded by subclasses.
"""
pass
def _act(self, action):
"""Perform a gym environment action on the simulation.
By default, this just passes the action through to the simulation, but it can be
overridden by subclasses for more complicated logic."""
self._set_action(action)
###############################################################################################
# Internal API - goal interface
def _next_goal(self):
""" Return next goal for the robot. Return a dictionary representing that goal """
current_state = self.goal_generation.current_state()
return self.goal_generation.next_goal(self._random_state, current_state)
def _calculate_goal_distance_reward(
self, previous_goal_distance, goal_distance
) -> float:
dist_reward = sum(
[
previous_goal_distance[k] - goal_distance[k]
for k in self.constants.success_threshold
]
)
return dist_reward
def _calculate_goal_distance(self, current_state):
goal_distance = self.goal_generation.goal_distance(self._goal, current_state)
return goal_distance
def _is_successful_state(self, current_state):
goal_distance = self._calculate_goal_distance(current_state)
return self._is_successful(goal_distance)
def _is_successful(self, goal_distance):
return all(
[
np.all(goal_distance[k] < self.constants.success_threshold[k])
for k in self.constants.success_threshold
]
)
def _get_goal_info(self):
""" Calculate information about current state of the goal """
current_state = self.goal_generation.current_state()
goal_distance = self._calculate_goal_distance(current_state)
relative_goal = goal_distance.pop("relative_goal", None)
goal_reachable = self.goal_generation.goal_reachable(self._goal, current_state)
# In case it's the first time, just set it to current goal distance
if self._previous_goal_distance is None:
self._previous_goal_distance = goal_distance
goal_distance_reward = self._calculate_goal_distance_reward(
self._previous_goal_distance, goal_distance
)
self._previous_goal_distance = goal_distance
optional_keys = {}
is_successful = (
self._is_successful(goal_distance)
or self.goal_generation.reached_terminal_state
)
optional_keys["goal_max_dist"] = {
k: np.max(goal_distance[k]) for k in self.constants.success_threshold
}
optional_keys["goal_failures"] = {
k: np.sum(goal_distance[k] > self.constants.success_threshold[k])
for k in self.constants.success_threshold
}
goal_info = {
"current_state": current_state,
"goal_dist": {key: np.sum(dist) for key, dist in goal_distance.items()},
"goal_achieved": is_successful,
"goal": self._goal,
"penalty": current_state.get("penalty", 0.0),
"goal_reachable": goal_reachable,
"solved": self.goal_generation.reached_terminal_state,
}
goal_info.update(optional_keys)
if relative_goal is not None:
for key, val in relative_goal.items():
goal_info[f"rel_goal_{key}"] = val.copy()
return goal_distance_reward, is_successful, deepcopy(goal_info)
@property
def _is_goal_achieved(self) -> bool:
"""
Return if current goal is achieved.
"""
assert self._goal_info_cache
return self._goal_info_cache[1]
@property
def _goal_info_dict(self) -> dict:
"""
Return dict containing info e.g. goal state, relative goal state etc. for current goal
"""
assert self._goal_info_cache
return self._goal_info_cache[2]
###############################################################################################
# Fully internal methods.
def _synchronize_step_time(self):
"""
Synchronize step time based on current threshold.
"""
# Figure out the frequency with which we would step the underlying simulation (in seconds).
delta_threshold_s = self._get_wall_clock_step_time_threshold()
# Sleep until we hit the time step (delta threshold)
current_time = time.time()
wait_time = max(0.0, delta_threshold_s - (current_time - self.last_update_time))
time.sleep(wait_time)
update_time = time.time()
self.last_update_time = update_time
def _get_wall_clock_step_time_threshold(self):
"""
Return the minimum threshold wall clock step time.
"""
if self.constants.physical:
sim = self.mujoco_simulation.mj_sim
return float(sim.nsubsteps) * sim.model.opt.timestep
else:
# No minimum threshold for simulation.
return 0
def _observe_sync(self, sync_type=SyncType.STEP):
"""
Sync all observation providers and return latest observation. Ideally this
should only be called once every step.
"""
self.mujoco_simulation.forward()
self.update_goal_info()
self.observer.sync(sync_type=sync_type)
observations = self.observe()
# Notify each robot of the new observations.
# This path allows robots to do whatever they need with this observations update. For example, certain arms
# that use sub-simulations can sync them with the new observations.
self.robot.on_observations_updated(observations)
return observations
###############################################################################################
# External API - to establish communication with other parts of the system
@property
def observation_space(self):
if self._observation_space is None:
obs = self._observe_sync(sync_type=SyncType.RESET)
self._observation_space = gym_space_from_arrays(obs)
return self._observation_space
@property
def robot(self):
return self._cached_robot
@property
def warnings(self):
""" List of MuJoCo warnings """
return self.warning_buffer.warnings
@property
def sim(self):
""" Define this property so that it plays nicely with other parts of the system """
return self.mujoco_simulation.sim
def observe(self) -> dict:
"""
Return read only view of current observation of the environment. Calling this method
multiple times in a row should yield same result without any side effect.
There are two ways to provide environment observations:
1. Via _observe_simple(): You can return observation using classic style gym observe
method where you can directly return data associate with each observation. The downside
of this approach it's not easy to change the observation based on different environment
configuration e.g. sim vs physical.
It's recommended to only return observations which are cheap to fetch and consistent
across all environment configurations in _observe_simple().
2. Via observer.observe(): This comes with a bit extra overhead as it requires creating
observation provider and observation classes. But it has better structured support for
observations can vary based on environment configuration or needs to be updated not at
every step.
It's recommended to handle all polymorphic observations in observer.
"""
assert self.observer
obs = self._observe_simple()
obs.update(self.observer.observe())
return self.randomization.observation_randomizer.randomize(
obs, self._random_state
)
def _observe_simple(self):
"""
Returns simple observations which can always be fetched regardless of which observation
providers are specified. This function is called every time observe is called so everything
here needs to cheap to fetch.
Good candidates to be included here are mujoco simulation state, info from goal dict etc.
"""
return {}
###############################################################################################
# External API - gym Env
def reset(self):
"""Resets the state of the environment and returns an initial observation.
Warning: Do not overload this method! It ensures operations happen in certain order.
please add custom reset logic to _reset.
Returns: observation (object): the initial observation of the
space.
"""
# Reset time counter
self.t = 0
# Reset randomization
self.randomization.reset()
# Randomize parameters.
self.parameters = self.randomization.parameter_randomizer.randomize(
self.parameters, self._random_state
)
self._reset()
# Randomize simulation. Because sim is recreated in self._reset(),
# simulation_randomizer.randomize should be called after the _reset.
self.randomization.simulation_randomizer.randomize(
self.mujoco_simulation.mj_sim, self._random_state
)
# reset observer.
self.observer.reset()
# Reset multi goal tracker for a new episode.
self.multi_goal_tracker.reset()
# Reset state of goal generation.
return self.reset_goal_generation(sync_type=SyncType.RESET)
def step_finalize(self, obs, env_reward, done, info):
# Process the output by multiple goal tracker.
goal_distance_reward, is_successful, goal_info = self.goal_info()
obs, reward, done, info = self.multi_goal_tracker.process(
obs, env_reward, done, info, goal_distance_reward, is_successful, goal_info
)
info.update(goal_info)
return obs, reward, done, info
def step(self, action):
"""Run one timestep of the environment's dynamics. When end of
episode is reached, you are responsible for calling `reset()`
to reset this environment's state.
Accepts an action and returns a tuple (observation, reward, done, info).
Args:
action (object): an action provided by the environment
Returns:
observation (object): agent's observation of the current environment
reward (float) : amount of reward returned after previous action
done (boolean): whether the episode has ended, in which case further step() calls will
return undefined results
info (dict): contains auxiliary diagnostic information (helpful for debugging, and
sometimes learning)
"""
action = self.randomization.action_randomizer.randomize(
action, self._random_state
)
robot_exception = None
try:
self._act(action)
except RobotException as re:
logger.error(
f"Robot raised exception: {str(re)}. This will finish the current episode."
)
robot_exception = re
if not self.constants.physical:
# We don't need to do stepping for physical roll out.
self.mujoco_simulation.step()
self._synchronize_step_time()
self.t += 1
obs, reward, done, info = self.get_observation(robot_exception=robot_exception)
obs, reward, done, info = self.step_finalize(obs, reward, done, info)
return obs, reward, done, info
def get_info_finalize(self, info: dict) -> dict:
"""This is called to append more info into the original `info` dict, including stats
from multi-goal tracker or self-play tracker.
This should neither affect observation nor trigger any tracker process calls.
Multiple calls without step() in-between should yield same results.
"""
_, _, goal_info = self.goal_info()
info = self.multi_goal_tracker.update_info(info, goal_info)
info.update(goal_info)
return info
def get_observation(self, robot_exception=None):
# Get current state to return to the user
obs = self._observe_sync()
if robot_exception is None:
info, env_reward, done = self.get_simulation_info_reward_with_done()
else:
# Robot raised an exception, we can't continue with this tick, since we can't assume that the robot
# performed the action.
info = {"robot_raised_exception": True}
done = True
env_reward = 0.0 # TBD consider adding a penalty if useful
info = self.get_info_finalize(info)
return obs, env_reward, done, info
def get_info(self):
obs, reward, done, info = self.get_observation()
return info
def get_simulation_info_reward_with_done(self):
info = self._get_simulation_info()
env_reward, done = self._get_simulation_reward_with_done(info)
assert isinstance(env_reward, float)
return info, env_reward, done
def update_goal_info(self):
"""
Re-computes and caches the current goal_info. Usually you do not have to call this
since `step` will automatically do so. However, if you manipulate the state externally,
you will have to call this method after.
"""
self._goal_info_cache = self._get_goal_info()
def reset_goal(self, update_seed=False, sync_type=SyncType.RESET_GOAL):
""" Reset the goal of the environment """
# Reset stats for one goal in the same episode.
self.multi_goal_tracker.reset_goal_steps()
# Randomize a target for the robot
self._goal = self._next_goal()
self._previous_goal_distance = None
return self._observe_sync(sync_type=sync_type)
def reset_goal_generation(self, sync_type=SyncType.RESET_GOAL):
""" Reset state of goal generation. """
self.goal_generation.reset(self._random_state)
return self.reset_goal(sync_type=sync_type)
def goal_info(self):
""" Return info about the goal """
return self._goal_info_cache
def render(self, mode="human", width=500, height=500):
"""Renders the environment.
The set of supported modes varies per environment. (And some
environments do not support rendering at all.) By convention,
if mode is:
- human: render to the current display or terminal and
return nothing. Usually for human consumption.
- rgb_array: Return an numpy.ndarray with shape (x, y, 3),
representing RGB values for an x-by-y pixel image, suitable
for turning into a video.
- ansi: Return a string (str) or StringIO.StringIO containing a
terminal-style text representation. The text can include newlines
and ANSI escape sequences (e.g. for colors).
Note:
Make sure that your class's metadata 'render.modes' key includes
the list of supported modes. It's recommended to call super()
in implementations to use the functionality of this method.
Args:
mode (str): the mode to render with
width (int): Width of the rendered image.
height (int): Height of the rendered image.
Example:
class MyEnv(Env):
metadata = {'render.modes': ['human', 'rgb_array']}
def render(self, mode='human'):
if mode == 'rgb_array':
return np.array(...) # return RGB frame suitable for video
elif mode is 'human':
... # pop up a window and render
else:
super(MyEnv, self).render(mode=mode) # just raise an exception
"""
if mode == "human":
return self.mujoco_simulation.mujoco_viewer.render()
elif mode == "rgb_array":
return self.mujoco_simulation.render(width=width, height=height)
else:
raise ValueError("Unsupported mode %s" % mode)
def seed(self, seed=None):
"""Sets the seed for this env's random number generator(s).
Note:
Some environments use multiple pseudorandom number generators.
We want to capture all such seeds used in order to ensure that
there aren't accidental correlations between multiple generators.
Returns:
list<bigint>: Returns the list of seeds used in this env's random
number generators. The first value in the list should be the
"main" seed, or the value which a reproducer should pass to
'seed'. Often, the main seed equals the provided 'seed', but
this won't be true if seed=None, for example.
"""
if isinstance(seed, list):
# Support list of seeds as required by Gym.
seed = seed[0]
elif isinstance(seed, int):
pass
elif seed is not None:
# If seed is None, we just return current seed.
raise ValueError("Seed must be an integer.")
if seed is not None:
self._last_seed = seed
self._random_state.seed(seed)
self.action_space.seed(seed)
# Return list of seeds to conform to Gym specs
return [self._last_seed]
def apply_wrappers(self, **wrapper_params):
"""
Apply wrappers to the environment.
"""
return self
@classmethod
@abc.abstractmethod
def build_robot(cls, mujoco_simulation: SType, physical: bool) -> Robot:
"""
Build robot for this environment.
"""
pass
@classmethod
@abc.abstractmethod
def build_goal_generation(
cls, constants: CType, mujoco_simulation: SType
) -> GoalGenerator:
"""
Build goal generation for this environment.
"""
pass
@classmethod
@abc.abstractmethod
def build_simulation(cls, constants: CType, parameters: PType) -> SType:
"""
Build simulation for this environment.
"""
pass
@classmethod
def build_mujoco_modifiers(cls) -> Dict[str, Modifier]:
"""
Build mujoco modifiers for this environment.
"""
return OrderedDict()
@classmethod
def build_randomization(
cls, constants: CType, parameters: PType
) -> EnvRandomization:
"""
Build simulation for this environment.
"""
return EnvRandomization(
parameter_randomizer=cls.build_parameter_randomizer(constants, parameters),
observation_randomizer=EnvObservationRandomizer(
cls.build_observation_randomizers(constants)
),
action_randomizer=EnvActionRandomizer(
cls.build_action_randomizers(constants)
),
simulation_randomizer=EnvSimulationRandomizer(
cls.build_simulation_randomizers(constants)
),
)
@classmethod
def build_parameter_randomizer(
cls, constants: CType, parameters: PType
) -> EnvParameterRandomizer:
"""
Build parameter randomizer for the environment.
"""
return EnvParameterRandomizer(parameters)
@classmethod
def build_observation_randomizers(cls, constants) -> List[ObservationRandomizer]:
"""
Build observation randomizers for the environment.
"""
return []
@classmethod
def build_action_randomizers(cls, constants) -> List[ActionRandomizer]:
"""
Build action randomizers for the environment.
"""
return []
@classmethod
def build_simulation_randomizers(cls, constants) -> List[SimulationRandomizer]:
"""
Build simulation randomizers for the environment.
"""
return []
@classmethod
def build(
cls,
parameters=None,
constants=None,
wrapper_params=None,
starting_seed=None,
apply_wrappers=True,
):
"""
Construct a dactyl environment together with a set of common wrappers.
"""
if parameters is None:
parameters = {}
if constants is None:
constants = {}
if wrapper_params is None:
wrapper_params = {}
parameter_class = get_generic_param_type(cls, 0, RobotEnvParameters)
constant_class = get_generic_param_type(cls, 1, RobotEnvConstants)
if isinstance(parameters, dict):
parameters = parameter_class(**parameters)
if isinstance(constants, dict):
constants = constant_class(**constants)
mujoco_simulation = cls.build_simulation(constants, parameters,)
mujoco_modifiers = cls.build_mujoco_modifiers()
goal_generation = cls.build_goal_generation(constants, mujoco_simulation)
randomization = cls.build_randomization(constants, parameters)
for name in constants.randomizers:
randomization.get_randomizer(name).enable()
robot = cls.build_robot(
mujoco_simulation=mujoco_simulation, physical=constants.physical
)
env = cls(
parameters=parameters,
constants=constants,
mujoco_simulation=mujoco_simulation,
mujoco_modifiers=mujoco_modifiers,
robot=robot,
goal_generation=goal_generation,
randomization=randomization,
starting_seed=starting_seed,
)
if apply_wrappers:
env = env.apply_wrappers(**wrapper_params)
return env
@classmethod
def _get_default_wrappers(cls):
return None
| 40,152 | 34.098776 | 115 | py |
robogym | robogym-master/robogym/mujoco/constants.py | from enum import Enum
OPT_FIELDS = {
"apirate",
"collision",
"cone",
"density",
"disableflags",
"enableflags",
"gravity",
"impedance",
"impratio",
"integrator",
"iterations",
"jacobian",
"magnetic",
"mpr_iterations",
"mpr_tolerance",
"noslip_iterations",
"noslip_tolerance",
"o_margin",
"o_solimp",
"o_solref",
"reference",
"solver",
"timestep",
"tolerance",
"uintptr",
"viscosity",
"wind",
}
"""
follow mujoco-py order:
cdef enum USER_DEFINED_ACTUATOR_PARAMS:
IDX_PROPORTIONAL_GAIN = 0,
IDX_INTEGRAL_TIME_CONSTANT = 1,
IDX_INTEGRAL_MAX_CLAMP = 2,
IDX_DERIVATIVE_TIME_CONSTANT = 3,
IDX_DERIVATIVE_GAIN_SMOOTHING = 4,
IDX_ERROR_DEADBAND = 5,
"""
PID_GAIN_PARAMS = [
"pid_kp",
"pid_ti",
"pid_imax_clamp",
"pid_td",
"pid_dsmooth",
"pid_error_deadband",
]
class MujocoEquality(Enum):
mjEQ_CONNECT = 0 # connect two bodies at a point (ball joint)
mjEQ_WELD = 1 # fix relative position and orientation of two bodies
mjEQ_JOINT = 2 # couple the values of two scalar joints with cubic
mjEQ_TENDON = 3 # couple the lengths of two tendons with cubic
mjEQ_DISTANCE = 4 # fix the contact distance betweent two geoms
| 1,292 | 19.854839 | 72 | py |
robogym | robogym-master/robogym/mujoco/simulation_interface.py | import itertools as it
from typing import Dict, List
import attr
from mujoco_py import MjSimState, cymj
from robogym.mujoco.helpers import (
joint_qpos_ids,
joint_qpos_ids_from_prefix,
joint_qvel_ids,
joint_qvel_ids_from_prefix,
)
from robogym.mujoco.mujoco_xml import MjSim
@attr.s(auto_attribs=True)
class SimulationParameters:
"""
Containing all parameters needed to build the mujoco simulation.
"""
pass
class SimulationInterface:
"""
Base class for domain-specific simulation interfaces tied to particular XML.
Goal is to transform code interfacing with generic `MjSim` that looks like that:
hand_angles = sim.data.qpos[hand_angle_idx]
cube_pos = sim.data.qpos[cube_pos_idx]
sim.model.actuator_gainprm[actuator_idx] = actuator_kps
sim.model.actuator_biasprm[actuator_idx] = actuator_kps
Into more high-level and domain-specific version:
hand_angles = sim.hand.get_angles()
cube_pos = sim.get_cube_pos()
sim.set_actuator_kp(actuator_kps)
Etc.
This is a base class that just exposes a few generic utilities to help the subclasses
implement the abovementioned functionality. By convention, the subclasses should be named
<Something>Simulation.
"""
__slots__ = [
"sim",
"qpos_idxs",
"qvel_idxs",
"synchronization_points",
"_mujoco_viewer",
]
def __init__(self, sim: MjSim):
self.sim = sim
self.qpos_idxs: Dict[str, List[int]] = {}
self.qvel_idxs: Dict[str, List[int]] = {}
self.synchronization_points = [] # type: ignore
self._mujoco_viewer = None
@property
def mj_sim(self):
""" MuJoCo simulation object - alias to make it clearer """
return self.sim
@property
def mujoco_viewer(self):
"""
Get a nicely-interactive version of the mujoco viewer
"""
if self._mujoco_viewer is None:
# Inline import since this is only relevant on platforms
# which have GLFW support.
from mujoco_py.mjviewer import MjViewer # noqa
self._mujoco_viewer = MjViewer(self.sim)
return self._mujoco_viewer
def enable_pid(self):
""" Enable our custom PID controller code for the actuators with 'user' type """
cymj.set_pid_control(self.sim.model, self.sim.data)
###############################################################################################
# SUBCLASS REGISTRATION
def register_joint_group(self, group_name, prefix):
""" Finds and collect joint ids for given joint name prefix or a list of prefixes. """
if isinstance(prefix, str):
self.qpos_idxs[group_name] = joint_qpos_ids_from_prefix(
self.sim.model, prefix
)
self.qvel_idxs[group_name] = joint_qvel_ids_from_prefix(
self.sim.model, prefix
)
elif isinstance(prefix, list):
self.qpos_idxs[group_name] = list(
it.chain.from_iterable(
joint_qpos_ids_from_prefix(self.sim.model, p) for p in prefix
)
)
self.qvel_idxs[group_name] = list(
it.chain.from_iterable(
joint_qvel_ids_from_prefix(self.sim.model, p) for p in prefix
)
)
def register_joint_group_by_name(self, group_name, name):
""" Finds and collect joint ids for given joint name or list of names. """
if isinstance(name, str):
self.qpos_idxs[group_name] = joint_qpos_ids(self.sim.model, name)
self.qvel_idxs[group_name] = joint_qvel_ids(self.sim.model, name)
elif isinstance(name, list):
self.qpos_idxs[group_name] = list(
it.chain.from_iterable(joint_qpos_ids(self.sim.model, n) for n in name)
)
self.qvel_idxs[group_name] = list(
it.chain.from_iterable(joint_qvel_ids(self.sim.model, n) for n in name)
)
###############################################################################################
# GET DATA OUT OF SIM
def get_qpos(self, group_name):
""" Gets qpos for a particular group. """
return self.sim.data.qpos[self.qpos_idxs[group_name]]
def get_qpos_dict(self, group_names):
""" Gets qpos dictionary for multiple groups. """
return {k: self.get_qpos(k) for k in group_names}
def get_qvel(self, group_name):
""" Gets qvel for a particular group. """
return self.sim.data.qvel[self.qvel_idxs[group_name]]
def get_qvel_dict(self, group_names):
""" Gets qpos dictionary for multiple groups. """
return {k: self.get_qvel(k) for k in group_names}
@property
def qpos(self):
""" Returns. copy of full sim qpos. """
return self.sim.data.qpos.copy()
@property
def qvel(self):
""" Returns copy of full sim qvel. """
return self.sim.data.qvel.copy()
def get_state(self) -> MjSimState:
return self.sim.get_state()
###############################################################################################
# SET DATA IN SIM
def set_qpos(self, group_name, value):
""" Sets qpos for a given group. """
self.sim.data.qpos[self.qpos_idxs[group_name]] = value
def set_qvel(self, group_name, value):
""" Sets qpos for a given group. """
self.sim.data.qvel[self.qvel_idxs[group_name]] = value
def add_qpos(self, group_name, value):
""" Sets qpos for a given group. """
self.sim.data.qpos[self.qpos_idxs[group_name]] += value
def set_state(self, state: MjSimState):
self.sim.set_state(state)
###############################################################################################
# INTERFACE TO UNDERLYING SIM
def step(self, with_udd=True):
"""
Advances the simulation by calling ``mj_step``.
If ``qpos`` or ``qvel`` have been modified directly, the user is required to call
:meth:`.forward` before :meth:`.step` if their ``udd_callback`` requires access to MuJoCo
state set during the forward dynamics.
"""
self.sim.step(with_udd=with_udd)
self.sim.forward()
# To potentially communicate with other processes
for point in self.synchronization_points:
point.synchronize()
def reset(self):
"""
Resets the simulation data and clears buffers.
"""
self.sim.reset()
def set_constants(self):
"""
Sets the derived constants of the mujoco simulation.
"""
self.sim.set_constants()
def forward(self):
"""
Computes the forward kinematics. Calls ``mj_forward`` internally.
"""
self.sim.forward()
def render(
self,
width=None,
height=None,
*,
camera_name=None,
depth=False,
mode="offscreen",
device_id=-1
):
"""
Renders view from a camera and returns image as an `numpy.ndarray`.
Args:
- width (int): desired image width.
- height (int): desired image height.
- camera_name (str): name of camera in model. If None, the free
camera will be used.
- depth (bool): if True, also return depth buffer
- device (int): device to use for rendering (only for GPU-backed
rendering).
Returns:
- rgb (uint8 array): image buffer from camera
- depth (float array): depth buffer from camera (only returned
if depth=True)
"""
return self.sim.render(
width=width,
height=height,
camera_name=camera_name,
depth=depth,
mode=mode,
device_id=device_id,
)
###############################################################################################
# PROPERTIES
@property
def n_substeps(self):
""" Number of substeps in the mujoco sim """
return self.sim.nsubsteps
| 8,178 | 31.585657 | 99 | py |
robogym | robogym-master/robogym/mujoco/mujoco_xml.py | import os.path
import typing
import xml.etree.ElementTree as et
import mujoco_py
import numpy as np
ASSETS_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../assets"))
XML_DIR = os.path.join(ASSETS_DIR, "xmls")
def _format_array(np_array, precision=6):
""" Format numpy array into a nice string suitable for mujoco XML """
if not isinstance(np_array, np.ndarray):
np_array = np.array(np_array, dtype=float)
# Make sure it's flattened
if len(np_array.shape) > 1:
np_array = np_array.reshape(-1)
if np.min(np.abs(np_array)) > 0.001:
format_str = "{:.%df}" % precision
else:
format_str = "{:.%de}" % precision
# Finally format a string out of numpy array
return " ".join(format_str.format(x) for x in np_array)
class StaleMjSimError(Exception):
"""
Exception indicating the MjSim instance is stale and should no longer be used.
"""
pass
class MjSim(mujoco_py.MjSim):
"""
There are environments e.g. rearrange environment which recreates
sim after reach env reset. This can cause potential bugs caused by
other components still caching instance of old sim. These bugs are usually
quite tricky to find. This class makes it easier to find these bugs by allowing
invalidating the sim instance so any access to properties of stale sim instance
will cause error.
"""
__slots__ = ("_stale", "_xml")
def __init__(self, model, **kwargs):
# Note: we don't need to call super.__init__ because MjSim use __cinit__
# for initialization which happens automatically before subclass __init__
# is called.
self._stale: bool = False
self._xml = model.get_xml()
def get_xml(self):
"""
Mujoco's internal get_xml() is unreliable as it seems to override the internal
memory buffer when more than one sim is instantiated. We therefore cache the model
xml on creation.
:return:
"""
return self._xml
def set_stale(self):
"""
Set this sim instance as stale so further access to properties of this
instance will raise error.
"""
self._stale = True
def is_stale(self):
return self._stale
@property
def data(self):
self._ensure_not_stale()
return super().data
@property
def model(self):
self._ensure_not_stale()
return super().model
def _ensure_not_stale(self):
if self._stale:
raise StaleMjSimError(
"You are accessing property of a stale sim instance which is no longer used"
"by the environment."
)
class MujocoXML:
"""
Class that combines multiple MuJoCo XML files into a single one.
"""
meshdir = os.path.join(ASSETS_DIR, "stls")
texturedir = os.path.join(ASSETS_DIR, "textures")
TEXTURE_ATTRIBUTES = [
"file",
"fileback" "filedown",
"filefront",
"fileleft",
"fileright",
"fileup",
]
NAMED_FIELDS = {
"actuator",
"body1",
"body2",
"childclass",
"class",
"geom",
"geom1",
"geom2",
"joint",
"joint1",
"joint2",
"jointparent",
"material",
"mesh",
"name",
"sidesite",
"site",
"source",
"target",
"tendon",
"texture",
}
###############################################################################################
# CONSTRUCTION
@classmethod
def parse(cls, xml_filename: str):
""" Parse given xml filename into the MujocoXML model """
xml_full_path = os.path.join(XML_DIR, xml_filename)
if not os.path.exists(xml_full_path):
raise Exception(xml_full_path)
with open(xml_full_path) as f:
xml_root = et.parse(f).getroot()
xml = cls(xml_root)
xml.load_includes(os.path.dirname(os.path.abspath(xml_full_path)))
return xml
@classmethod
def from_string(cls, contents: str):
""" Construct MujocoXML from string """
xml_root = et.XML(contents)
xml = cls(xml_root)
xml.load_includes()
return xml
def __init__(self, root_element: typing.Optional[et.Element] = None):
""" Create new MujocoXML class """
# This is the root element of the XML document we'll be modifying
if root_element is None:
# Create empty root element
self.root_element = et.Element("mujoco")
else:
# Initialize it from the existing thing
self.root_element = root_element
###############################################################################################
# COMBINING MUJOCO ELEMENTS
def add_default_compiler_directive(self):
""" Add a default compiler directive """
self.root_element.append(
et.Element(
"compiler",
{
"meshdir": self.meshdir,
"texturedir": self.texturedir,
"angle": "radian",
"coordinate": "local",
},
)
)
return self
def append(self, other: "MujocoXML"):
""" Append another XML object to this object """
self.root_element.extend(other.root_element)
return self
def xml_string(self):
""" Return combined XML as a string """
return et.tostring(self.root_element, encoding="unicode", method="xml")
def load_includes(self, include_root=""):
"""
Some mujoco files contain includes that need to be process on our side of the system
Find all elements that have an 'include' child
"""
for element in self.root_element.findall(".//include/.."):
# Remove in a second pass to avoid modifying list while iterating it
elements_to_remove_insert = []
for idx, subelement in enumerate(element):
if subelement.tag == "include":
# Branch off initial filename
include_path = os.path.join(include_root, subelement.get("file"))
include_element = MujocoXML.parse(include_path)
elements_to_remove_insert.append(
(idx, subelement, include_element.root_element)
)
# Iterate in reversed order to make sure indices are not screwed up
for idx, to_remove, to_insert in reversed(elements_to_remove_insert):
element.remove(to_remove)
to_insert_list = list(to_insert)
# Insert multiple elements
for i in range(len(to_insert)):
element.insert(idx + i, to_insert_list[i])
return self
def _resolve_asset_paths(self, meshdir, texturedir):
"""Resolve relative asset path in xml to local file path."""
for mesh in self.root_element.findall(".//mesh"):
fname = mesh.get("file")
if fname is not None:
if fname[0] != "/":
fname = os.path.join(meshdir or self.meshdir, fname)
mesh.set("file", fname)
for texture in self.root_element.findall(".//texture"):
for attribute in self.TEXTURE_ATTRIBUTES:
fname = texture.get(attribute)
if fname is not None:
if fname[0] != "/":
fname = os.path.join(texturedir or self.texturedir, fname)
texture.set(attribute, fname)
def build(self, output_filename=None, meshdir=None, texturedir=None, **kwargs):
""" Build and return a mujoco simulation """
self._resolve_asset_paths(meshdir, texturedir)
xml_string = self.xml_string()
if output_filename is not None:
with open(output_filename, "wt") as f:
f.write(xml_string)
mj_model = mujoco_py.load_model_from_xml(xml_string)
return MjSim(mj_model, **kwargs)
###############################################################################################
# MODIFICATIONS
def set_objects_attr(self, tag: str = "*", **kwargs):
""" Set given attribute to all instances of given tag within the tree """
for element in self.root_element.findall(".//{}".format(tag)):
for name, value in kwargs.items():
if isinstance(value, (list, np.ndarray)):
value = _format_array(value)
element.set(name, str(value))
return self
def set_objects_attrs(self, tag_args: dict):
"""
Batch version of set_objects_attr where args for multiple tags can be specified as a dict.
"""
for tag, args in tag_args.items():
self.set_objects_attr(tag=tag, **args)
def set_named_objects_attr(self, name: str, tag: str = "*", **kwargs):
""" Sets xml attributes of all objects with given name """
for element in self.root_element.findall(".//{}[@name='{}']".format(tag, name)):
for name, value in kwargs.items():
if isinstance(value, (list, np.ndarray)):
value = _format_array(value)
element.set(name, str(value))
return self
def set_prefixed_objects_attr(self, prefix: str, tag: str = "*", **kwargs):
""" Sets xml attributes of all objects with given name prefix """
for element in self.root_element.findall(".//{}[@name]".format(tag)):
if element.get("name").startswith(prefix): # type: ignore
for name, value in kwargs.items():
if isinstance(value, (list, np.ndarray)):
value = _format_array(value)
element.set(name, str(value))
return self
def add_name_prefix(self, name_prefix: str, exclude_attribs=[]):
"""
Add a given name prefix to all elements with "name" attribute.
Additionally, once we changed all "name" attributes we also have to change all
attribute fields that refer to those names.
"""
for element in self.root_element.iter():
for attrib_name in element.keys():
if (
attrib_name not in self.NAMED_FIELDS
or attrib_name in exclude_attribs
):
continue
element.set(attrib_name, name_prefix + element.get(attrib_name)) # type: ignore
return self
def replace_name(self, old_name: str, new_name: str, exclude_attribs=[]):
"""
Replace an old name string with an new name string in "name" attribute.
"""
for element in self.root_element.iter():
for attrib_name in element.keys():
if (
attrib_name not in self.NAMED_FIELDS
or attrib_name in exclude_attribs
):
continue
element.set(attrib_name, element.get(attrib_name).replace(old_name, new_name)) # type: ignore
return self
def remove_objects_by_tag(self, tag: str):
""" Remove objects with given tag from XML """
for element in self.root_element.findall(".//{}/..".format(tag)):
for subelement in list(element):
if subelement.tag != tag:
continue
assert subelement.tag == tag
element.remove(subelement)
return self
def remove_objects_by_prefix(self, prefix: str, tag: str = "*"):
""" Remove objects with given name prefix from XML """
for element in self.root_element.findall(".//{}[@name]/..".format(tag)):
for subelement in list(element):
if subelement.get("name").startswith(prefix): # type: ignore
element.remove(subelement)
return self
def remove_objects_by_name(
self, names: typing.Union[typing.List[str], str], tag: str = "*"
):
""" Remove object with given name from XML """
if isinstance(names, str):
names = [names]
for name in names:
for element in self.root_element.findall(
".//{}[@name='{}']/..".format(tag, name)
):
for subelement in list(element):
if subelement.get("name") == name:
element.remove(subelement)
return self
| 12,646 | 32.635638 | 110 | py |
robogym | robogym-master/robogym/mujoco/warning_buffer.py | import collections
import logging
import mujoco_py.cymj as cymj
logger = logging.getLogger(__name__)
class MujocoErrorException(Exception):
""" Exception raised when mujoco error is called. """
pass
def error_callback(message):
""" Mujoco error callback """
message = message.decode()
full_message = f"MUJOCO ERROR: {message}"
logger.error(full_message)
raise MujocoErrorException(full_message)
# Set it once for all the processes
cymj.set_error_callback(error_callback)
class MjWarningBuffer:
"""
Buffering MuJoCo warnings.
That way they don't cause an exception being thrown which crashes the process,
but at the same time we store them in memory and can process.
One can potentially specify buffer capacity if one wants to use a circular buffer.
"""
def __init__(self, maxlen=None):
self.maxlen = maxlen
self._buffer = collections.deque(maxlen=self.maxlen)
self._prev_user_callback = None
def _intercept_warning(self, warn_bytes):
""" Intercept a warning """
warn = warn_bytes.decode() # Convert bytes to string
logger.warning("MUJOCO WARNING: %s", str(warn))
self._buffer.append(warn)
@property
def warnings(self):
""" Return a list of warnings to the user """
return list(self._buffer)
def enter(self):
""" Enable collecting warnings """
if self._prev_user_callback is None:
self._prev_user_callback = cymj.get_warning_callback()
cymj.set_warning_callback(self._intercept_warning)
def clear(self):
""" Reset warning buffer """
self._buffer.clear()
def exit(self):
""" Stop collecting warnings """
if self._prev_user_callback is not None:
cymj.set_warning_callback(self._prev_user_callback)
self._prev_user_callback = None
def __enter__(self):
""" Enter - context manager magic method """
self.enter()
def __exit__(self, exc_type, exc_val, exc_tb):
""" Exit - context manager magic method """
self.exit()
def __repr__(self):
""" Text representation"""
return "<{} warnings:{}>".format(self.__class__.__name__, len(self.warnings))
| 2,265 | 25.97619 | 86 | py |
robogym | robogym-master/robogym/mujoco/forward_kinematics.py | import xml.etree.ElementTree as et
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
import robogym.utils.rotation as rot
from robogym.mujoco.mujoco_xml import MujocoXML
def homogeneous_matrix_from_pos_mat(pos, mat):
m = np.eye(4)
m[:3, :3] = mat
m[:3, 3] = pos
return m
def get_joint_matrix(pos, angle, axis):
def transform_rot_x_matrix(pos, angle):
"""
Optimization - create a homogeneous matrix where rotation submatrix
rotates around the X axis by given angle in radians
"""
m = np.eye(4)
m[1, 1] = m[2, 2] = np.cos(angle)
s = np.sin(angle)
m[1, 2] = -s
m[2, 1] = s
m[:3, 3] = pos
return m
def transform_rot_y_matrix(pos, angle):
"""
Optimization - create a homogeneous matrix where rotation submatrix
rotates around the Y axis by given angle in radians
"""
m = np.eye(4)
m[0, 0] = m[2, 2] = np.cos(angle)
s = np.sin(angle)
m[0, 2] = s
m[2, 0] = -s
m[:3, 3] = pos
return m
def transform_rot_z_matrix(pos, angle):
"""
Optimization - create a homogeneous matrix where rotation submatrix
rotates around the Z axis by given angle in radians
"""
m = np.eye(4)
m[0, 0] = m[1, 1] = np.cos(angle)
s = np.sin(angle)
m[0, 1] = -s
m[1, 0] = s
m[:3, 3] = pos
return m
if abs(axis[0]) == 1.0 and axis[1] == 0.0 and axis[2] == 0.0:
return transform_rot_x_matrix(pos, angle * axis[0])
elif axis[0] == 0.0 and abs(axis[1]) == 1.0 and axis[2] == 0.0:
return transform_rot_y_matrix(pos, angle * axis[1])
elif axis[0] == 0.0 and axis[1] == 0.0 and abs(axis[2]) == 1.0:
return transform_rot_z_matrix(pos, angle * axis[2])
else:
return homogeneous_matrix_from_pos_mat(
pos, rot.quat2mat(rot.quat_from_angle_and_axis(angle, axis))
)
class ForwardKinematics:
"""
Generic forward kinematics calculator for open chain rigid systems.
"""
def __init__(self, site_computations, joint_info):
self.site_computations = site_computations
self.joint_info = joint_info
def compute(self, qpos, return_joint_pos=False):
"""
given joint positions, calculate the endpoint positions.
currently only return positions, but orientation also computed
but just not returned.
later could be useful for other end effectors.
currently only support hinge joints.
"""
num_sites = len(self.site_computations)
site_positions = []
joint_positions = [None] * len(self.joint_info)
def cached_joint_calculator(computations, cidx):
joint_idx = computations[cidx]
assert isinstance(
joint_idx, int
), "computation should be body interweaving with joints."
if joint_positions[joint_idx] is not None:
return joint_positions[joint_idx]
else:
(joint_pos, joint_axis) = self.joint_info[joint_idx]
joint_matrix = get_joint_matrix(joint_pos, qpos[joint_idx], joint_axis)
if cidx == len(computations) - 2:
joint_pos = computations[-1] @ joint_matrix
else:
joint_pos = (
cached_joint_calculator(computations, cidx + 2)
@ computations[cidx + 1]
@ joint_matrix
)
joint_positions[joint_idx] = joint_pos
return joint_pos
for i in range(num_sites):
computations = self.site_computations[i]
m = computations[0]
if len(computations) > 1:
m = cached_joint_calculator(computations, 1) @ m
# only return position for now
site_positions.append(m[:3, 3])
if return_joint_pos:
joint_xpos = list(map(lambda v: v[:3, 3], joint_positions))
return np.array(site_positions + joint_xpos)
else:
return np.array(site_positions)
@classmethod
def prepare(
cls,
mxml: MujocoXML,
root_body_name: str,
root_body_pos: np.array,
root_body_euler: np.array,
target_sites: List[str],
joint_names: List[str],
):
"""
parse mujoco xml to build up the kinematic tree, also does some
static computations (e.g. fixed body/body connection).
target_sites are the endpoints to compute later
joint_names are sequence of joints passed at runtime for
the endpoint position calculations.
"""
IDENTITY_QUAT = rot.quat_identity()
ROOT_BODY_PARENT = "NONE"
target_sites_idx: Dict[str, int] = {
v: idx for idx, v in enumerate(target_sites)
}
joint_names_idx: Dict[str, int] = {v: idx for idx, v in enumerate(joint_names)}
num_sites = len(target_sites)
site_info: List[Optional[Tuple]] = [None] * num_sites # (4d matrix, parentBody)
joint_info: List[Optional[Tuple]] = [None] * len(joint_names) # (axis, pos)
body_info: Dict[
str, Any
] = dict() # name => (4d homegeneous matrix, parentbody)
body_joints: Dict[str, str] = dict() # body => joints
def get_matrix(x: et.Element):
pos = np.fromstring(x.attrib.get("pos"), sep=" ")
if "euler" in x.attrib:
euler = np.fromstring(x.attrib.get("euler"), sep=" ")
return homogeneous_matrix_from_pos_mat(pos, rot.euler2mat(euler))
elif "axisangle" in x.attrib:
axis_angle = np.fromstring(x.attrib.get("axisangle"), sep=" ")
quat = rot.quat_from_angle_and_axis(
axis_angle[-1], np.array(axis_angle[:-1])
)
return homogeneous_matrix_from_pos_mat(pos, rot.quat2mat(quat))
elif "quat" in x.attrib:
quat = np.fromstring(x.attrib.get("quat"), sep=" ")
return homogeneous_matrix_from_pos_mat(pos, rot.quat2mat(quat))
else:
quat = IDENTITY_QUAT
return homogeneous_matrix_from_pos_mat(pos, rot.quat2mat(quat))
def traverse(rt: et.Element, parent_body: str):
assert rt.tag == "body", "only start from body tag in xml"
matrix = get_matrix(rt)
name = rt.attrib.get("name", "noname_body_%d" % len(body_info))
body_info[name] = (matrix, parent_body)
for x in rt.findall("joint"):
joint_name = x.attrib.get("name", "")
joint_idx: int = joint_names_idx.get(joint_name, -1)
if joint_idx == -1:
continue
assert (
x.attrib.get("type", "hinge") == "hinge"
), "currently only support hinge joints"
pos = np.fromstring(x.attrib.get("pos"), sep=" ")
axis = np.fromstring(x.attrib.get("axis"), sep=" ")
joint_info[joint_idx] = (pos, axis)
assert (
joint_name not in body_joints
), "Only support open chain system, unsupported rigid bodies"
body_joints[name] = joint_name
for x in rt.findall("site"):
site_idx = target_sites_idx.get(x.attrib.get("name", ""), -1)
if site_idx != -1:
matrix = get_matrix(x)
site_info[site_idx] = (matrix, name)
for x in rt.findall("body"):
# recursive scan through body parts
traverse(x, name)
rt = None
for child in mxml.root_element.find("worldbody").findall("body"): # type: ignore
if child.attrib.get("name", "") == root_body_name:
rt = child
break
assert rt is not None, "no root body found in xml"
traverse(rt, ROOT_BODY_PARENT)
root_matrix = homogeneous_matrix_from_pos_mat(
root_body_pos, rot.euler2mat(root_body_euler)
)
# build the computation flow
site_computations = [[] for i in range(num_sites)] # type: ignore
for i in range(num_sites):
(matrix, parent_body) = site_info[i] # type: ignore # Just have to trust the code
while parent_body != ROOT_BODY_PARENT:
parent_matrix, new_parent_body = body_info[parent_body]
joint_name = body_joints.get(parent_body, "")
if joint_name:
site_computations[i].append(matrix)
site_computations[i].append(joint_names_idx[joint_name])
matrix = parent_matrix
else:
matrix = parent_matrix @ matrix
parent_body = new_parent_body
site_computations[i].append(root_matrix @ matrix)
return cls(site_computations, joint_info)
| 9,205 | 35.387352 | 94 | py |
robogym | robogym-master/robogym/mujoco/helpers.py | import itertools
import typing
import mujoco_py
import mujoco_py.generated.const
def joint_qpos_ids(model, joint_name: str) -> typing.List[int]:
addr = model.get_joint_qpos_addr(joint_name)
if isinstance(addr, tuple):
return list(range(addr[0], addr[1]))
else:
return [addr]
def joint_qpos_ids_from_prefix(model, joint_prefix):
qpos_ids_list = [
joint_qpos_ids(model, name)
for name in model.joint_names
if name.startswith(joint_prefix)
]
return list(itertools.chain.from_iterable(qpos_ids_list))
def joint_qvel_ids(model, joint_name: str) -> typing.List[int]:
addr = model.get_joint_qvel_addr(joint_name)
if isinstance(addr, tuple):
return list(range(addr[0], addr[1]))
else:
return [addr]
def joint_qvel_ids_from_prefix(model, joint_prefix):
qvel_ids_list = [
joint_qvel_ids(model, name)
for name in model.joint_names
if name.startswith(joint_prefix)
]
return list(itertools.chain.from_iterable(qvel_ids_list))
def joint_type_name(joint_type: int) -> str:
if joint_type == mujoco_py.generated.const.JNT_FREE:
return "free"
if joint_type == mujoco_py.generated.const.JNT_BALL:
return "ball"
if joint_type == mujoco_py.generated.const.JNT_SLIDE:
return "slide"
if joint_type == mujoco_py.generated.const.JNT_HINGE:
return "hinge"
raise AssertionError(f"unsupported joint type: {joint_type}")
| 1,484 | 27.018868 | 65 | py |
robogym | robogym-master/robogym/mujoco/test/test_mujoco_utils.py | import random
import numpy as np
from mujoco_py import cymj, functions
from numpy.random.mtrand import _rand as global_randstate
from robogym.mujoco.forward_kinematics import ForwardKinematics
from robogym.mujoco.mujoco_xml import MujocoXML
from robogym.mujoco.simulation_interface import SimulationInterface
from robogym.utils.rotation import uniform_quat
XML_BALL = """
<mujoco>
<worldbody>
<body name="ball">
<freejoint name="ball_joint"/>
<geom name="sphere" pos="0.00 0.00 0.00" type="sphere" size="0.1 0.1 0.1"/>
</body>
</worldbody>
</mujoco>
"""
XML_ARM = """
<mujoco>
<worldbody>
<body name="arm">
<joint type="hinge" name="hinge_joint" axis="0 0 1"/>
<geom name="sphere" pos="0.00 0.00 0.00" type="sphere" size="0.1 0.1 0.1"/>
<body name="forearm" pos="1 0 0">
<joint type="slide" axis="1 0 0" name="slide_joint"/>
<geom name="box" pos="0.00 0.00 0.00" type="box" size="0.1 0.1 0.1"/>
</body>
</body>
</worldbody>
</mujoco>
"""
def test_simple_mujoco_setup():
ball_one = (
MujocoXML.from_string(XML_BALL)
.add_name_prefix("ball_one:")
.set_named_objects_attr("ball_one:ball", pos=[1, 0, 0])
)
ball_two = (
MujocoXML.from_string(XML_BALL)
.add_name_prefix("ball_two:")
.set_named_objects_attr("ball_two:ball", pos=[-1, 0, 0])
)
main = (
MujocoXML().add_default_compiler_directive().append(ball_one).append(ball_two)
)
simulation = SimulationInterface(main.build())
simulation.register_joint_group("ball_one", "ball_one:ball_joint")
simulation.register_joint_group("ball_two", "ball_two:ball_joint")
assert simulation.get_qpos("ball_one").shape == (7,)
assert simulation.get_qpos("ball_two").shape == (7,)
assert simulation.get_qvel("ball_one").shape == (6,)
assert simulation.get_qvel("ball_two").shape == (6,)
qpos1 = np.random.randn(3)
qrot1 = uniform_quat(global_randstate)
qpos1_combined = np.concatenate([qpos1, qrot1])
qpos2 = np.random.randn(3)
qrot2 = uniform_quat(global_randstate)
qpos2_combined = np.concatenate([qpos2, qrot2])
simulation.set_qpos("ball_one", qpos1_combined)
simulation.set_qpos("ball_two", qpos2_combined)
assert np.linalg.norm(simulation.get_qpos("ball_one") - qpos1_combined) < 1e-6
assert np.linalg.norm(simulation.get_qpos("ball_two") - qpos2_combined) < 1e-6
def test_more_complex_mujoco_setup():
xml = (
MujocoXML()
.add_default_compiler_directive()
.append(
MujocoXML.from_string(XML_ARM)
.add_name_prefix("arm_one:")
.set_named_objects_attr("arm_one:ball", pos=[0, 1, 0])
)
.append(
MujocoXML.from_string(XML_ARM)
.add_name_prefix("arm_two:")
.set_named_objects_attr("arm_two:ball", pos=[0, -1, 0])
)
)
simulation = SimulationInterface(xml.build())
simulation.register_joint_group("arm_one", "arm_one:")
simulation.register_joint_group("arm_one_hinge", "arm_one:hinge_joint")
simulation.register_joint_group("arm_two", "arm_two:")
simulation.register_joint_group("arm_two_hinge", "arm_two:hinge_joint")
assert simulation.get_qpos("arm_one").shape == (2,)
assert simulation.get_qvel("arm_one").shape == (2,)
assert simulation.get_qpos("arm_two").shape == (2,)
assert simulation.get_qvel("arm_two").shape == (2,)
assert simulation.get_qpos("arm_one_hinge").shape == (1,)
assert simulation.get_qvel("arm_one_hinge").shape == (1,)
assert simulation.get_qpos("arm_two_hinge").shape == (1,)
assert simulation.get_qvel("arm_two_hinge").shape == (1,)
initial_qpos_one = simulation.get_qpos("arm_one")
initial_qpos_two = simulation.get_qpos("arm_two")
simulation.set_qpos("arm_one_hinge", 0.1)
# Chech that we are setting the right hinge joint
assert np.linalg.norm(simulation.get_qpos("arm_one") - initial_qpos_one) > 0.09
assert np.linalg.norm(simulation.get_qpos("arm_two") - initial_qpos_two) < 1e-6
def test_set_attributes_mixed_precision():
main = (
MujocoXML()
.add_default_compiler_directive()
.append(
MujocoXML.from_string(XML_BALL).set_named_objects_attr(
"ball", pos=[1, 1e-8, 1e-12]
)
)
)
simulation = SimulationInterface(main.build())
ball_id = simulation.sim.model.body_name2id("ball")
ball_pos = simulation.sim.model.body_pos[ball_id]
target_pos = np.array([1, 1e-8, 1e-12])
# test relative error cause absolute error can be quite small either way
assert np.linalg.norm((ball_pos / target_pos) - 1) < 1e-6
def test_forward_kinematics_on_inverted_pendulum():
mxml = MujocoXML.parse(
"test/inverted_pendulum/inverted_double_pendulum.xml"
).add_name_prefix("ivp:")
simulation = SimulationInterface(mxml.build())
simulation.register_joint_group("pendulum", "ivp:")
joint_names = list(map(lambda x: "ivp:%s" % x, ["hinge", "hinge2"]))
site_names = list(map(lambda x: "ivp:%s" % x, ["hinge2_site", "tip"]))
KIN = ForwardKinematics.prepare(
mxml, "ivp:cart", np.zeros(3), np.zeros(3), site_names, joint_names
)
for _ in range(5):
simulation.mj_sim.data.ctrl[0] = random.random()
for _ in range(100):
simulation.step()
simulation.forward()
site_positions = np.array(
[simulation.mj_sim.data.get_site_xpos(site) for site in site_names]
)
joint_pos = simulation.get_qpos("pendulum")
kinemetics_positions = KIN.compute(joint_pos, return_joint_pos=True)
assert (np.abs(site_positions - kinemetics_positions[:2]) < 1e-6).all()
assert (np.abs(site_positions[0] - kinemetics_positions[-1]) < 1e-6).all()
def test_remove_elem():
ball_without_joint = MujocoXML.from_string(XML_BALL).remove_objects_by_tag(
"freejoint"
)
ref_xml = """
<mujoco>
<worldbody>
<body name="ball">
<geom name="sphere" pos="0.00 0.00 0.00" size="0.1 0.1 0.1" type="sphere" />
</body>
</worldbody>
</mujoco>
"""
assert ref_xml.strip() == ball_without_joint.xml_string().strip()
def test_mj_error_callback():
message = None
called = False
def callback(msg):
nonlocal message
message = msg.decode()
raise RuntimeError(message)
cymj.set_error_callback(callback)
try:
with cymj.wrap_mujoco_warning():
functions.mju_error("error")
except RuntimeError as e:
assert e.args[0] == "error"
assert message == "error"
called = True
assert called
| 6,728 | 29.726027 | 86 | py |
robogym | robogym-master/robogym/mujoco/modifiers/base.py | class Modifier:
""" Base class for various MuJoCo modifiers """
def __init__(self):
self.sim = None
def initialize(self, sim):
self.sim = sim
def __call__(self, parameter_value):
""" Apply given parameter to the sim """
raise NotImplementedError
| 297 | 21.923077 | 51 | py |
robogym | robogym-master/robogym/mujoco/modifiers/timestep.py | from robogym.mujoco.modifiers.base import Modifier
class TimestepModifier(Modifier):
""" Modify simulation timestep """
def __call__(self, timestep):
self.sim.model.opt.timestep = timestep
| 208 | 22.222222 | 50 | py |
robogym | robogym-master/robogym/envs/dactyl/full_perpendicular.py | import functools
import typing
import attr
import numpy as np
import pycuber
import robogym.utils.rotation as rotation
from robogym.envs.dactyl.common.cube_env import (
CubeEnv,
CubeSimulationInterface,
DactylCubeEnvConstants,
DactylCubeEnvParameters,
)
from robogym.envs.dactyl.common.cube_manipulator import CubeManipulator
from robogym.envs.dactyl.common.mujoco_modifiers import PerpendicularCubeSizeModifier
from robogym.envs.dactyl.goals.face_cube_solver import FaceCubeSolverGoal
from robogym.envs.dactyl.goals.face_curriculum import FaceCurriculumGoal
from robogym.envs.dactyl.goals.face_free import FaceFreeGoal
from robogym.envs.dactyl.goals.fixed_fair_scramble import FixedFairScrambleGoal
from robogym.envs.dactyl.goals.full_unconstrained import FullUnconstrainedGoal
from robogym.envs.dactyl.goals.release_cube_solver import ReleaseCubeSolverGoal
from robogym.envs.dactyl.goals.unconstrained_cube_solver import UnconstrainedCubeSolver
from robogym.envs.dactyl.observation.cube import (
GoalCubeRotObservation,
MujocoCubePosObservation,
MujocoCubeRotObservation,
)
from robogym.envs.dactyl.observation.full_perpendicular import (
GoalCubePosObservation,
GoalFaceAngleObservation,
MujocoFaceAngleObservation,
)
from robogym.envs.dactyl.observation.shadow_hand import (
MujocoShadowhandAngleObservation,
MujocoShadowhandRelativeFingertipsObservation,
)
from robogym.goal.goal_generator import GoalGenerator
from robogym.mujoco.mujoco_xml import MujocoXML
from robogym.observation.mujoco import MujocoQposObservation, MujocoQvelObservation
from robogym.robot_env import ObservationMapValue as omv
@attr.s(auto_attribs=True)
class FullPerpendicularEnvParameters(DactylCubeEnvParameters):
""" Parameters of the Dactyl Perpendicular env - possible to change for each episode """
# How many steps with random action do we take when the environment is initialized
n_random_initial_steps: int = 10
# Multiplier of the cube size
cube_size_multiplier: float = 1.0
@attr.s(auto_attribs=True)
class FullPerpendicularEnvConstants(DactylCubeEnvConstants):
""" Parameters of the Dactyl Perpendicular env - set once and for all """
# Number of mujoco simulation steps per environment step
mujoco_substeps: int = 10
# How many steps with zero action to take on env reset
reset_initial_steps: int = 20
# Threshold for success conditions
success_threshold: dict = {"cube_quat": 0.4, "cube_face_angle": 0.2}
max_timesteps_per_goal: int = 1600
# What kind of goal generation we want for the environment
goal_generation: str = "face_free"
# Which directions do we rotate the faces
goal_directions: typing.List[str] = ["cw", "ccw"]
# Are faces always rotated to round angles
round_target_face: bool = True
# Probability of cube reorient vs face rotation
p_face_flip: float = 0.5
# How many times to scramble cube initially
num_scramble_steps: int = 50
# Whether to scramble face angles at the beginning of each episode
scramble_face_angles: bool = True
# Whether to randomize face angles at the beginning of each episode.
randomize_face_angles: bool = True
class FullPerpendicularSimulation(CubeSimulationInterface):
"""
Simulation of a shadow hand manipulating full perpendicular cube
"""
@classmethod
def _build_mujoco_cube_xml(cls, xml, cube_xml_path):
xml.append(
MujocoXML.parse(cube_xml_path)
.add_name_prefix("cube:")
.set_named_objects_attr("cube:middle", tag="body", pos=[1.0, 0.87, 0.2])
# Delete springs for now
.remove_objects_by_prefix(prefix="cube:cubelet:spring:", tag="joint")
)
# Target
xml.append(
MujocoXML.parse(cube_xml_path)
.add_name_prefix("target:")
.set_named_objects_attr("target:middle", tag="body", pos=[1.0, 0.87, 0.2])
# Delete springs for now
.remove_objects_by_prefix(prefix="target:cubelet:spring:", tag="joint")
# Disable collisions
.set_objects_attr(tag="geom", group="2", conaffinity="0", contype="0")
)
def __init__(self, sim):
super().__init__(sim)
self.cube_model = CubeManipulator(prefix="cube:", sim=sim)
self.target_model = CubeManipulator(prefix="target:", sim=sim)
self.register_joint_group("cube_position", prefix="cube:cube:t")
self.register_joint_group("cube_rotation", prefix="cube:cube:rot")
self.register_joint_group("cube_drivers", prefix="cube:cubelet:driver:")
self.register_joint_group("cube_cubelets", prefix="cube:cubelet:")
self.register_joint_group("target_position", prefix="target:cube:t")
self.register_joint_group("target_rotation", prefix="target:cube:rot")
self.register_joint_group("target_drivers", prefix="target:cubelet:driver:")
self.register_joint_group("target_cubelets", prefix="target:cubelet:")
self.register_joint_group("cube_all_joints", prefix="cube:")
self.register_joint_group("target_all_joints", prefix="target:")
self.register_joint_group("hand_angle", prefix="robot0:")
def clone_target_from_cube(self):
""" Clone target internal state from cube state """
self.set_qpos("target_cubelets", self.get_qpos("cube_cubelets"))
def get_face_angles(self, target):
""" Return "face angles" either from cube or from the target """
assert target in {"cube", "target"}
return self.get_qpos("{}_drivers".format(target))
def align_target_faces(self):
""" Align target orientation to given set of **straight** angles """
self.target_model.soft_align_faces()
def rotate_target_face(self, axis, side, angle):
""" Rotate given face of the target by given angle """
self.target_model.rotate_face(axis, side, angle)
class FullPerpendicularEnv(
CubeEnv[
FullPerpendicularEnvParameters,
FullPerpendicularEnvConstants,
FullPerpendicularSimulation,
]
):
"""
A dactyl Rubik's cube environment that aims to replicate physically accurately
perpendicular rotations
"""
# Face angle is six numbers: one number for each cube face
TARGET_ANGLE_SHAPE = 6
FACE_GEOM_NAMES = [
"cube:cubelet:neg_x",
"cube:cubelet:pos_x",
"cube:cubelet:neg_y",
"cube:cubelet:pos_y",
"cube:cubelet:neg_z",
"cube:cubelet:pos_z",
]
PYCUBER_ACTIONS = ["L", "L'", "R", "R'", "F", "F'", "B", "B'", "D", "D'", "U", "U'"]
def _default_observation_map(self):
return {
"cube_pos": omv({"mujoco": MujocoCubePosObservation}),
"cube_quat": omv({"mujoco": MujocoCubeRotObservation}),
"cube_face_angle": omv({"mujoco": MujocoFaceAngleObservation}),
"qpos": omv({"mujoco": MujocoQposObservation}),
"qvel": omv({"mujoco": MujocoQvelObservation}),
"perp_qpos": omv({"mujoco": MujocoQposObservation}), # Duplicate of qpos.
"perp_qvel": omv({"mujoco": MujocoQvelObservation}), # Duplicate of qvel.
"hand_angle": omv({"mujoco": MujocoShadowhandAngleObservation}),
"fingertip_pos": omv(
{"mujoco": MujocoShadowhandRelativeFingertipsObservation}
),
"goal_pos": omv({"goal": GoalCubePosObservation}),
"goal_quat": omv({"goal": GoalCubeRotObservation}),
"goal_face_angle": omv({"goal": GoalFaceAngleObservation}),
}
@classmethod
def build_goal_generation(
cls,
constants: FullPerpendicularEnvConstants,
mujoco_simulation: CubeSimulationInterface,
) -> GoalGenerator:
""" Construct a goal generation object """
if constants.goal_generation == "face_curr":
return FaceCurriculumGoal(
mujoco_simulation=mujoco_simulation,
success_threshold=constants.success_threshold,
face_geom_names=cls.FACE_GEOM_NAMES,
goal_directions=constants.goal_directions,
round_target_face=constants.round_target_face,
p_face_flip=constants.p_face_flip,
)
elif constants.goal_generation == "face_free":
return FaceFreeGoal(
mujoco_simulation=mujoco_simulation,
success_threshold=constants.success_threshold,
face_geom_names=cls.FACE_GEOM_NAMES,
goal_directions=constants.goal_directions,
round_target_face=constants.round_target_face,
p_face_flip=constants.p_face_flip,
)
elif constants.goal_generation == "face_cube_solver":
return FaceCubeSolverGoal(
mujoco_simulation=mujoco_simulation,
success_threshold=constants.success_threshold,
face_geom_names=cls.FACE_GEOM_NAMES,
num_scramble_steps=constants.num_scramble_steps,
)
elif constants.goal_generation == "release_cube_solver":
return ReleaseCubeSolverGoal(
mujoco_simulation=mujoco_simulation,
success_threshold=constants.success_threshold,
face_geom_names=cls.FACE_GEOM_NAMES,
num_scramble_steps=constants.num_scramble_steps,
)
elif constants.goal_generation == "full_unconstrained":
return FullUnconstrainedGoal(
mujoco_simulation=mujoco_simulation,
success_threshold=constants.success_threshold,
face_geom_names=cls.FACE_GEOM_NAMES,
goal_directions=constants.goal_directions,
round_target_face=constants.round_target_face,
)
elif constants.goal_generation == "unconstrained_cube_solver":
return UnconstrainedCubeSolver(
mujoco_simulation=mujoco_simulation,
success_threshold=constants.success_threshold,
face_geom_names=cls.FACE_GEOM_NAMES,
num_scramble_steps=constants.num_scramble_steps,
)
elif constants.goal_generation == "fixed_fair_scramble":
return FixedFairScrambleGoal(
mujoco_simulation=mujoco_simulation,
success_threshold=constants.success_threshold,
face_geom_names=cls.FACE_GEOM_NAMES,
num_scramble_steps=constants.num_scramble_steps,
)
else:
raise RuntimeError(
"Invalid 'goal_generation' constant '{}'".format(
constants.goal_generation
)
)
@classmethod
def build_simulation(cls, constants, parameters):
return FullPerpendicularSimulation.build(
n_substeps=constants.mujoco_substeps,
simulation_params=parameters.simulation_params,
)
@classmethod
def build_mujoco_modifiers(cls):
modifiers = super().build_mujoco_modifiers()
modifiers["cube_size_multiplier"] = PerpendicularCubeSizeModifier("cube:")
return modifiers
###############################################################################################
# Internal API - to be overridden - environment randomization
def _scramble_cube(self):
""" Scramble a cube randomly at the beginning of an episode """
cube = pycuber.Cube()
for i in range(self.constants.num_scramble_steps):
action = self._random_state.choice(self.PYCUBER_ACTIONS)
cube.perform_step(action)
self.mujoco_simulation.cube_model.from_pycuber(cube)
def _scramble_face_angles(self):
""" Scramble face angles randomly without moving cubelets in any way """
random_angles = self._random_state.choice([-2, -1, 0, 1, 2], size=6) * np.pi / 2
self.mujoco_simulation.set_qpos("cube_drivers", random_angles)
def _randomize_cube_initial_position(self):
""" Draw a random initial position for a cube """
# Original env had this, but I'm not really sure it's needed
for i in range(self.constants.reset_initial_steps):
ctrl = self.mujoco_simulation.shadow_hand.denormalize_position_control(
self.mujoco_simulation.shadow_hand.zero_control()
)
self.mujoco_simulation.shadow_hand.set_position_control(ctrl)
self.mujoco_simulation.step()
cube_translation = (
self._random_state.randn(3) * self.parameters.cube_position_wiggle_std
)
self.mujoco_simulation.add_qpos("cube_position", cube_translation)
cube_orientation = rotation.uniform_quat(self._random_state)
self.mujoco_simulation.set_qpos("cube_rotation", cube_orientation)
self._scramble_cube()
if self.constants.scramble_face_angles:
self._scramble_face_angles()
if self.constants.randomize_face_angles:
# Face angles
random_face_angle = self._random_state.uniform(
-np.pi / 4, np.pi / 4, size=2
)
# Face axes
random_axis = self._random_state.randint(3)
self.mujoco_simulation.cube_model.rotate_face(
random_axis, 0, random_face_angle[0]
)
self.mujoco_simulation.cube_model.rotate_face(
random_axis, 1, random_face_angle[1]
)
# Need to call this after the qpos is modified
self.mujoco_simulation.forward()
action = self._random_state.uniform(-1.0, 1.0, self.action_space.shape[0])
for _ in range(self.parameters.n_random_initial_steps):
ctrl = self.mujoco_simulation.shadow_hand.denormalize_position_control(
action
)
self.mujoco_simulation.shadow_hand.set_position_control(ctrl)
self.mujoco_simulation.step()
###############################################################################################
# External API - to establish communication with other parts of the system
@property
def cube_type(self):
""" Type of cube """
return "full-perpendicular"
@property
def face_joint_names(self):
# Needed by some wrappers
return [
# Need to drop 'cube:' prefix
x[5:]
for x in self.mujoco_simulation.cube_model.joints
]
###############################################################################################
# Fully internal methods
def _render_callback(self, _sim, _viewer):
""" Set a render callback """
self.mujoco_simulation.set_qpos("target_position", np.array([0.15, 0, -0.03]))
self.mujoco_simulation.set_qpos("target_rotation", self._goal["cube_quat"])
self.mujoco_simulation.set_qpos("target_drivers", self._goal["cube_face_angle"])
self.mujoco_simulation.set_qvel("target_all_joints", 0.0)
self.mujoco_simulation.forward()
@classmethod
def _get_default_wrappers(cls):
default_wrappers = super()._get_default_wrappers()
default_wrappers.update(
{
"default_observation_noise_levels": {
"fingertip_pos": {"uncorrelated": 0.002, "additive": 0.001},
"hand_angle": {"additive": 0.1, "uncorrelated": 0.1},
"cube_pos": {"additive": 0.005, "uncorrelated": 0.001},
"cube_quat": {"additive": 0.1, "uncorrelated": 0.09},
"cube_face_angle": {"additive": 0.1, "uncorrelated": 0.1},
},
"default_no_noise_levels": {
"fingertip_pos": {},
"hand_angle": {},
"cube_pos": {},
"cube_quat": {},
"cube_face_angle": {},
},
"default_observation_delay_levels": {
"interpolators": {
"cube_quat": "QuatInterpolator",
"cube_face_angle": "RadianInterpolator",
},
"groups": {
# Uncomment below to enable observation delay randomization.
# "vision": {
# "obs_names": ["cube_pos", "cube_quat"],
# "mean": 3,
# "std": 0.5,
# },
# "giiker": {
# "obs_names": ["cube_face_angle"],
# "mean": 1,
# "std": 0.2,
# },
# "phasespace": {
# "obs_names": ["fingertip_pos"],
# "mean": 0.5,
# "std": 0.1,
# }
},
},
"default_no_observation_delay_levels": {
"interpolators": {},
"groups": {},
},
"pre_obsnoise_randomizations": [
["RandomizedActionLatency"],
["RandomizedPerpendicularCubeSizeWrapper"],
["RandomizedBodyInertiaWrapper"],
["RandomizedTimestepWrapper"],
["RandomizedRobotFrictionWrapper"],
["RandomizedCubeFrictionWrapper"],
["RandomizedGravityWrapper"],
["RandomizedWindWrapper"],
["RandomizedPhasespaceFingersWrapper"],
["RandomizedRobotDampingWrapper"],
["RandomizedRobotKpWrapper"],
["RandomizedFaceDampingWrapper"],
["RandomizedJointLimitWrapper"],
["RandomizedTendonRangeWrapper"],
],
}
)
return default_wrappers
make_simple_env = functools.partial(FullPerpendicularEnv.build, apply_wrappers=False)
make_env = FullPerpendicularEnv.build
| 18,202 | 39.541203 | 99 | py |
robogym | robogym-master/robogym/envs/dactyl/reach.py | import functools
import typing
import attr
import numpy as np
from robogym.envs.dactyl.observation.reach import (
GoalFingertipPosObservation,
GoalIsAchievedObservation,
)
from robogym.envs.dactyl.observation.shadow_hand import (
MujocoShadowhandAbsoluteFingertipsObservation,
MujocoShadowHandJointPosObservation,
MujocoShadowHandJointVelocityObservation,
)
from robogym.goal.goal_generator import GoalGenerator
from robogym.mujoco.mujoco_xml import MujocoXML
from robogym.mujoco.simulation_interface import SimulationInterface
from robogym.observation.goal import GoalObservationProvider
from robogym.observation.mujoco import MujocoObservationProvider, ObservationProvider
from robogym.robot.shadow_hand.hand_forward_kinematics import FINGERTIP_SITE_NAMES
from robogym.robot.shadow_hand.mujoco.mujoco_shadow_hand import MuJoCoShadowHand
from robogym.robot_env import ObservationMapValue as omv
from robogym.robot_env import RobotEnv, RobotEnvConstants, RobotEnvParameters
from robogym.wrappers import dactyl, randomizations, util
DEFAULT_NOISE_LEVELS: typing.Dict[str, dict] = {
"fingertip_pos": {"uncorrelated": 0.001, "additive": 0.001},
}
NO_NOISE_LEVELS: typing.Dict[str, dict] = {
key: {} for key in DEFAULT_NOISE_LEVELS.keys()
}
@attr.s(auto_attribs=True)
class ReachEnvParameters(RobotEnvParameters):
""" Parameters of the shadow hand reach env - possible to change for each episode. """
pass
@attr.s(auto_attribs=True)
class ReachEnvConstants(RobotEnvConstants):
""" Parameters of the shadow hand reach env - same for all episodes. """
success_threshold: dict = {"fingertip_pos": 0.025}
# If specified, freeze all other fingers.
active_finger: typing.Optional[str] = None
# Overwrite the following constants regarding rewards.
successes_needed: int = 50
max_timesteps_per_goal: int = 150
class ReachSimulation(SimulationInterface):
"""
Simulation interface for shadow hand reach env.
"""
# Just a floor
FLOOR_XML = "floor/basic_floor.xml"
# Target fingertip sites.
TARGET_XML = "shadowhand_reach/target.xml"
# Robot hand xml
HAND_XML = "robot/shadowhand/main.xml"
# XML with default light
LIGHT_XML = "light/default.xml"
def __init__(self, sim):
super().__init__(sim)
self.enable_pid()
self.shadow_hand = MuJoCoShadowHand(self)
@classmethod
def build(cls, n_substeps: int = 10):
"""Construct a ShadowHandReachSimulation object.
:param n_substeps: (int) sim.nsubsteps, num of substeps
:return: a ShadowHandReachSimulation object with properly constructed sim.
"""
xml = MujocoXML()
xml.add_default_compiler_directive()
xml.append(
MujocoXML.parse(cls.FLOOR_XML).set_named_objects_attr(
"floor", tag="body", pos=[1, 1, 0]
)
)
target = MujocoXML.parse(cls.TARGET_XML)
colors = [
[1.0, 0.0, 0.0, 1.0],
[0.0, 1.0, 0.0, 1.0],
[0.0, 0.0, 1.0, 1.0],
[1.0, 1.0, 0.0, 1.0],
[1.0, 0.0, 1.0, 1.0],
]
for site, color in zip(FINGERTIP_SITE_NAMES, colors):
target.set_named_objects_attr(
f"target_{site}",
pos=[0.5, 0.5, 0.0],
type="sphere",
rgba=color,
size=0.005,
)
xml.append(target)
xml.append(
MujocoXML.parse(cls.HAND_XML)
.add_name_prefix("robot0:")
.set_named_objects_attr(
"robot0:hand_mount",
tag="body",
pos=[1.0, 1.25, 0.15],
euler=[np.pi / 2, 0, np.pi],
)
.remove_objects_by_name("robot0:annotation:outer_bound")
# Remove hand base free joint so that hand is immovable
.remove_objects_by_name("robot0:hand_base")
)
xml.append(MujocoXML.parse(cls.LIGHT_XML))
simulation = cls(xml.build(nsubsteps=n_substeps))
# Move fingers out of the way.
simulation.shadow_hand.set_position_control(
simulation.shadow_hand.denormalize_position_control(
simulation.shadow_hand.zero_control()
)
)
for _ in range(20):
simulation.step()
return simulation
class ReachEnv(RobotEnv[ReachEnvParameters, ReachEnvConstants, ReachSimulation]):
"""
Environment with the ShadowHand and a locked cube (i.e. no moving pieces, just a solid
block).
"""
def _build_observation_providers(self):
"""
Initialize observation providers for the environment.
"""
providers: typing.Dict[str, ObservationProvider] = {
"mujoco": MujocoObservationProvider(self.mujoco_simulation),
"goal": GoalObservationProvider(lambda: self.goal_info()),
}
return providers
def _default_observation_map(self):
return {
"qpos": omv({"mujoco": MujocoShadowHandJointPosObservation}),
"qvel": omv({"mujoco": MujocoShadowHandJointVelocityObservation}),
"fingertip_pos": omv(
{"mujoco": MujocoShadowhandAbsoluteFingertipsObservation}
),
"goal_fingertip_pos": omv({"goal": GoalFingertipPosObservation}),
"is_goal_achieved": omv({"goal": GoalIsAchievedObservation}),
}
@classmethod
def build_goal_generation(
cls, constants, mujoco_simulation: ReachSimulation
) -> GoalGenerator:
""" Construct a goal generation object """
goal_simulation = ReachSimulation.build(n_substeps=mujoco_simulation.n_substeps)
sim = goal_simulation.mj_sim
# Make sure fingers are separated.
# For transfer, want to make sure post-noise locations are achievable.
sim.model.geom_margin[:] = sim.model.geom_margin + 0.002
from robogym.envs.dactyl.goals.shadow_hand_reach_fingertip_pos import (
FingertipPosGoal,
)
return FingertipPosGoal(mujoco_simulation, goal_simulation)
@classmethod
def build_simulation(cls, constants, parameters):
return ReachSimulation.build(n_substeps=constants.mujoco_substeps)
@classmethod
def build_robot(cls, mujoco_simulation, physical):
return mujoco_simulation.shadow_hand
def _render_callback(self, _sim, _viewer):
""" Set a render callback """
goal_fingertip_pos = self._goal["fingertip_pos"].reshape(-1, 3)
for finger_idx, site in enumerate(FINGERTIP_SITE_NAMES):
goal_pos = goal_fingertip_pos[finger_idx]
site_id = _sim.model.site_name2id(f"target_{site}")
_sim.data.site_xpos[site_id] = goal_pos
def _reset(self):
super()._reset()
self.constants.success_pause_range_s = (0.0, 0.5)
def apply_wrappers(self, **wrapper_params):
"""
Apply wrappers to the environment.
"""
self.constants: ReachEnvConstants
env = util.ClipActionWrapper(self)
if self.constants.active_finger is not None:
env = dactyl.FingerSeparationWrapper(
env, active_finger=self.constants.active_finger
)
if self.constants.randomize:
env = randomizations.RandomizedActionLatency(env)
env = randomizations.RandomizedBodyInertiaWrapper(env)
env = randomizations.RandomizedTimestepWrapper(env)
env = randomizations.RandomizedRobotFrictionWrapper(env)
env = randomizations.RandomizedGravityWrapper(env)
env = dactyl.RandomizedPhasespaceFingersWrapper(env)
env = dactyl.RandomizedRobotDampingWrapper(env)
env = dactyl.RandomizedRobotKpWrapper(env)
noise_levels = DEFAULT_NOISE_LEVELS
else:
noise_levels = NO_NOISE_LEVELS
# must happen before angle observation wrapper
env = randomizations.RandomizeObservationWrapper(env, levels=noise_levels)
if self.constants.randomize:
env = dactyl.FingersFreezingPhasespaceMarkers(env)
env = randomizations.ActionNoiseWrapper(env)
env = util.SmoothActionWrapper(
env
) # this get's applied before noise is added (important)
env = util.RelativeGoalWrapper(env)
env = util.UnifiedGoalObservationWrapper(env, goal_parts=["fingertip_pos"])
env = util.ClipObservationWrapper(env)
env = util.ClipRewardWrapper(env)
env = util.PreviousActionObservationWrapper(env)
env = util.DiscretizeActionWrapper(
env, n_action_bins=self.constants.n_action_bins
)
# Note: Recording wrapper is removed here to favor simplicity.
return env
make_simple_env = functools.partial(ReachEnv.build, apply_wrappers=False)
make_env = ReachEnv.build
| 8,954 | 32.920455 | 90 | py |
robogym | robogym-master/robogym/envs/dactyl/face_perpendicular.py | import functools
import logging
from typing import List
import attr
import numpy as np
import robogym.utils.rotation as rotation
from robogym.envs.dactyl.common.cube_env import (
CubeEnv,
CubeSimulationInterface,
DactylCubeEnvConstants,
DactylCubeEnvParameters,
)
from robogym.envs.dactyl.common.mujoco_modifiers import PerpendicularCubeSizeModifier
from robogym.envs.dactyl.goals.face_curriculum import FaceCurriculumGoal
from robogym.envs.dactyl.goals.face_free import FaceFreeGoal
from robogym.envs.dactyl.observation.cube import (
GoalCubeRotObservation,
MujocoCubePosObservation,
MujocoCubeRotObservation,
)
from robogym.envs.dactyl.observation.face_perpendicular import (
GoalCubePosObservation,
GoalFaceAngleObservation,
MujocoFaceAngleObservation,
)
from robogym.envs.dactyl.observation.shadow_hand import (
MujocoShadowhandAngleObservation,
MujocoShadowhandRelativeFingertipsObservation,
)
from robogym.goal.goal_generator import GoalGenerator
from robogym.mujoco.mujoco_xml import MujocoXML
from robogym.observation.mujoco import MujocoQposObservation, MujocoQvelObservation
from robogym.robot_env import ObservationMapValue as omv
logger = logging.getLogger(__name__)
@attr.s(auto_attribs=True)
class FacePerpendicularEnvParameters(DactylCubeEnvParameters):
""" Parameters of the Dactyl Face Perpendicular env - possible to change for each episode"""
pass
@attr.s(auto_attribs=True)
class FacePerpendicularEnvConstants(DactylCubeEnvConstants):
""" Parameters of the Dactyl Perpendicular env - set once and for all """
# Threshold for success conditions
success_threshold: dict = {"cube_quat": 0.4, "cube_face_angle": 0.2}
# What kind of goal generation we want for the environment
goal_generation: str = "face_curr"
#####################
# Curriculum settings
# Which directions do we rotate the faces
goal_directions: List[str] = ["cw", "ccw"]
# Are faces always rotated to round angles
round_target_face: bool = True
# Probability of cube reorient vs face rotation
p_face_flip: float = 0.25
class FacePerpendicularSimulation(CubeSimulationInterface):
"""
Simulation of a shadow hand manipulating a face cube
"""
@classmethod
def _build_mujoco_cube_xml(cls, xml, cube_xml_path):
xml.append(
MujocoXML.parse(cube_xml_path)
.add_name_prefix("cube:")
.set_named_objects_attr("cube:middle", tag="body", pos=[1.0, 0.87, 0.2])
# Leave +/- z driver joints
.remove_objects_by_name(names="cube:cubelet:driver:neg_x", tag="joint")
.remove_objects_by_name(names="cube:cubelet:driver:pos_x", tag="joint")
.remove_objects_by_name(names="cube:cubelet:driver:neg_y", tag="joint")
.remove_objects_by_name(names="cube:cubelet:driver:pos_y", tag="joint")
# Remove x/y cubelet hinge joints
.remove_objects_by_prefix(prefix="cube:cubelet:rotx:", tag="joint")
.remove_objects_by_prefix(prefix="cube:cubelet:roty:", tag="joint")
# Delete springs for now
.remove_objects_by_prefix(prefix="cube:cubelet:spring:", tag="joint")
# Remove remaining cubelet joints we're not interested in
.remove_objects_by_name(
names=[
"cube:cubelet:rotz:neg_x_pos_y",
"cube:cubelet:rotz:neg_x_neg_y",
"cube:cubelet:rotz:pos_x_pos_y",
"cube:cubelet:rotz:pos_x_neg_y",
],
tag="joint",
)
)
# Target
xml.append(
MujocoXML.parse(cube_xml_path)
.add_name_prefix("target:")
.set_named_objects_attr("target:middle", tag="body", pos=[1.0, 0.87, 0.2])
# Disable collisions
.set_objects_attr(tag="geom", group="2", conaffinity="0", contype="0")
# Leave +/- z driver joints
.remove_objects_by_name(names="target:cubelet:driver:neg_x", tag="joint")
.remove_objects_by_name(names="target:cubelet:driver:pos_x", tag="joint")
.remove_objects_by_name(names="target:cubelet:driver:neg_y", tag="joint")
.remove_objects_by_name(names="target:cubelet:driver:pos_y", tag="joint")
# Remove x/y cubelet hinge joints
.remove_objects_by_prefix(prefix="target:cubelet:rotx:", tag="joint")
.remove_objects_by_prefix(prefix="target:cubelet:roty:", tag="joint")
.remove_objects_by_prefix(prefix="target:cubelet:spring:", tag="joint")
# Remove remaining cubelet joints we're not interested in
.remove_objects_by_name(
names=[
"target:cubelet:rotz:neg_x_pos_y",
"target:cubelet:rotz:neg_x_neg_y",
"target:cubelet:rotz:pos_x_pos_y",
"target:cubelet:rotz:pos_x_neg_y",
],
tag="joint",
)
)
def __init__(self, mujoco_simulation):
super().__init__(mujoco_simulation)
self.register_joint_group("cube_position", prefix="cube:cube:t")
self.register_joint_group("cube_rotation", prefix="cube:cube:rot")
self.register_joint_group_by_name(
"cube_top_face_driver", name="cube:cubelet:driver:pos_z"
)
self.register_joint_group_by_name(
"cube_bottom_face_driver", name="cube:cubelet:driver:neg_z"
)
self.register_joint_group_by_name(
"cube_drivers",
name=["cube:cubelet:driver:pos_z", "cube:cubelet:driver:neg_z"],
)
self.register_joint_group_by_name(
"cube_top_face",
name=[
"cube:cubelet:driver:pos_z",
"cube:cubelet:rotz:neg_x_pos_y_pos_z",
"cube:cubelet:rotz:neg_x_neg_y_pos_z",
"cube:cubelet:rotz:neg_x_pos_z",
"cube:cubelet:rotz:pos_x_pos_z",
"cube:cubelet:rotz:pos_x_neg_y_pos_z",
"cube:cubelet:rotz:pos_x_pos_y_pos_z",
"cube:cubelet:rotz:neg_y_pos_z",
"cube:cubelet:rotz:pos_y_pos_z",
],
)
self.register_joint_group_by_name(
"cube_bottom_face",
name=[
"cube:cubelet:driver:neg_z",
"cube:cubelet:rotz:neg_x_pos_y_neg_z",
"cube:cubelet:rotz:neg_x_neg_y_neg_z",
"cube:cubelet:rotz:neg_x_neg_z",
"cube:cubelet:rotz:pos_x_neg_z",
"cube:cubelet:rotz:pos_x_neg_y_neg_z",
"cube:cubelet:rotz:pos_x_pos_y_neg_z",
"cube:cubelet:rotz:neg_y_neg_z",
"cube:cubelet:rotz:pos_y_neg_z",
],
)
self.register_joint_group("cube_springs", prefix="cube:cubelet:spring:")
self.register_joint_group("target_position", prefix="target:cube:t")
self.register_joint_group("target_rotation", prefix="target:cube:rot")
self.register_joint_group_by_name(
"target_top_face_driver", name="target:cubelet:driver:pos_z"
)
self.register_joint_group_by_name(
"target_bottom_face_driver", name="target:cubelet:driver:neg_z"
)
self.register_joint_group_by_name(
"target_drivers",
name=["target:cubelet:driver:pos_z", "target:cubelet:driver:neg_z"],
)
self.register_joint_group_by_name(
"target_top_face",
name=[
"target:cubelet:driver:pos_z",
"target:cubelet:rotz:neg_x_pos_y_pos_z",
"target:cubelet:rotz:neg_x_neg_y_pos_z",
"target:cubelet:rotz:neg_x_pos_z",
"target:cubelet:rotz:pos_x_pos_z",
"target:cubelet:rotz:pos_x_neg_y_pos_z",
"target:cubelet:rotz:pos_x_pos_y_pos_z",
"target:cubelet:rotz:neg_y_pos_z",
"target:cubelet:rotz:pos_y_pos_z",
],
)
self.register_joint_group_by_name(
"target_bottom_face",
name=[
"target:cubelet:driver:neg_z",
"target:cubelet:rotz:neg_x_pos_y_neg_z",
"target:cubelet:rotz:neg_x_neg_y_neg_z",
"target:cubelet:rotz:neg_x_neg_z",
"target:cubelet:rotz:pos_x_neg_z",
"target:cubelet:rotz:pos_x_neg_y_neg_z",
"target:cubelet:rotz:pos_x_pos_y_neg_z",
"target:cubelet:rotz:neg_y_neg_z",
"target:cubelet:rotz:pos_y_neg_z",
],
)
self.register_joint_group("target_springs", prefix="target:cubelet:spring:")
self.register_joint_group("target_all_joints", prefix="target:")
self.register_joint_group("hand_angle", prefix="robot0:")
def set_face_angles(self, target, angles):
assert target in {"cube", "target"}
self.set_qpos("{}_top_face".format(target), angles[0])
self.set_qpos("{}_bottom_face".format(target), angles[1])
def get_face_angles(self, target):
assert target in {"cube", "target"}
return self.get_qpos("{}_drivers".format(target))
def rotate_target_face(self, side, angle):
""" Rotate given face of the target by given angle """
qpos = self.get_face_angles("target")
qpos[side] += angle
self.set_face_angles("target", qpos)
def clone_target_from_cube(self):
""" Clone target internal state from cube state """
self.set_face_angles("target", self.get_face_angles("cube"))
def align_target_faces(self):
""" Align target orientation to straight orientation of the cube """
self.set_face_angles(
"target", rotation.round_to_straight_angles(self.get_face_angles("target"))
)
class FacePerpendicularEnv(
CubeEnv[
FacePerpendicularEnvParameters,
FacePerpendicularEnvConstants,
FacePerpendicularSimulation,
]
):
"""
A dactyl Rubik's cube environment that aims to replicate dactyl face env using simpler
code
"""
# Target angle is two numbers: top face angle and bottom face angle
TARGET_ANGLE_SHAPE = 2
FACE_GEOM_NAMES = ["cube:cubelet:pos_z", "cube:cubelet:neg_z"]
FACE_JOINT_NAMES = [
"cubelet:driver:pos_z",
"cubelet:rotz:neg_x_pos_y_pos_z",
"cubelet:rotz:neg_x_neg_y_pos_z",
"cubelet:rotz:neg_x_pos_z",
"cubelet:rotz:pos_x_pos_z",
"cubelet:rotz:pos_x_neg_y_pos_z",
"cubelet:rotz:pos_x_pos_y_pos_z",
"cubelet:rotz:neg_y_pos_z",
"cubelet:rotz:pos_y_pos_z",
"cubelet:driver:neg_z",
"cubelet:rotz:neg_x_pos_y_neg_z",
"cubelet:rotz:neg_x_neg_y_neg_z",
"cubelet:rotz:neg_x_neg_z",
"cubelet:rotz:pos_x_neg_z",
"cubelet:rotz:pos_x_neg_y_neg_z",
"cubelet:rotz:pos_x_pos_y_neg_z",
"cubelet:rotz:neg_y_neg_z",
"cubelet:rotz:pos_y_neg_z",
]
def _default_observation_map(self):
return {
"cube_pos": omv({"mujoco": MujocoCubePosObservation}),
"cube_quat": omv({"mujoco": MujocoCubeRotObservation}),
"cube_face_angle": omv({"mujoco": MujocoFaceAngleObservation}),
"qpos": omv({"mujoco": MujocoQposObservation}),
"qvel": omv({"mujoco": MujocoQvelObservation}),
"perp_qpos": omv({"mujoco": MujocoQposObservation}), # Duplicate of qpos.
"perp_qvel": omv({"mujoco": MujocoQvelObservation}), # Duplicate of qvel.
"hand_angle": omv({"mujoco": MujocoShadowhandAngleObservation}),
"fingertip_pos": omv(
{"mujoco": MujocoShadowhandRelativeFingertipsObservation}
),
"goal_pos": omv({"goal": GoalCubePosObservation}),
"goal_quat": omv({"goal": GoalCubeRotObservation}),
"goal_face_angle": omv({"goal": GoalFaceAngleObservation}),
}
@classmethod
def build_goal_generation(
cls,
constants: FacePerpendicularEnvConstants,
mujoco_simulation: FacePerpendicularSimulation,
) -> GoalGenerator:
""" Construct a goal generation object """
if constants.goal_generation == "face_curr":
return FaceCurriculumGoal(
mujoco_simulation=mujoco_simulation,
success_threshold=constants.success_threshold,
face_geom_names=cls.FACE_GEOM_NAMES,
goal_directions=constants.goal_directions,
round_target_face=constants.round_target_face,
p_face_flip=constants.p_face_flip,
)
elif constants.goal_generation == "face_free":
return FaceFreeGoal(
mujoco_simulation=mujoco_simulation,
success_threshold=constants.success_threshold,
face_geom_names=cls.FACE_GEOM_NAMES,
goal_directions=constants.goal_directions,
round_target_face=constants.round_target_face,
p_face_flip=constants.p_face_flip,
)
else:
raise RuntimeError(
"Invalid 'goal_generation' constant '{}'".format(
constants.goal_generation
)
)
@classmethod
def build_simulation(cls, constants, parameters):
return FacePerpendicularSimulation.build(
n_substeps=constants.mujoco_substeps,
simulation_params=parameters.simulation_params,
)
@classmethod
def build_mujoco_modifiers(cls):
modifiers = super().build_mujoco_modifiers()
modifiers["cube_size_multiplier"] = PerpendicularCubeSizeModifier("cube:")
return modifiers
###############################################################################################
# Internal API - to be overridden - environment randomization
def _randomize_cube_initial_position(self):
""" Draw a random initial position for a cube """
# Original env had this, but I'm not really sure it's needed
for i in range(self.constants.reset_initial_steps):
ctrl = self.mujoco_simulation.shadow_hand.denormalize_position_control(
self.mujoco_simulation.shadow_hand.zero_control()
)
self.mujoco_simulation.shadow_hand.set_position_control(ctrl)
self.mujoco_simulation.step()
cube_translation = (
self._random_state.randn(3) * self.parameters.cube_position_wiggle_std
)
self.mujoco_simulation.add_qpos("cube_position", cube_translation)
cube_orientation = rotation.uniform_quat(self._random_state)
self.mujoco_simulation.set_qpos("cube_rotation", cube_orientation)
random_face_angle = self._random_state.uniform(-np.pi / 4, np.pi / 4, 2)
self.mujoco_simulation.set_face_angles("cube", random_face_angle)
# Need to call this after the qpos is modified
self.mujoco_simulation.forward()
action = self._random_state.uniform(-1.0, 1.0, self.action_space.shape[0])
for _ in range(self.parameters.n_random_initial_steps):
ctrl = self.mujoco_simulation.shadow_hand.denormalize_position_control(
action
)
self.mujoco_simulation.shadow_hand.set_position_control(ctrl)
self.mujoco_simulation.step()
###############################################################################################
# External API - to establish communication with other parts of the system
@property
def cube_type(self):
""" Type of cube """
return "face-perpendicular"
@property
def face_joint_names(self):
""" Used by some wrapper """
return self.FACE_JOINT_NAMES
###############################################################################################
# Fully internal methods
def _render_callback(self, _sim, _viewer):
""" Set a render callback """
self.mujoco_simulation.set_qpos("target_position", np.array([0.15, 0, -0.03]))
self.mujoco_simulation.set_qpos("target_rotation", self._goal["cube_quat"])
self.mujoco_simulation.set_face_angles("target", self._goal["cube_face_angle"])
self.mujoco_simulation.set_qvel("target_position", 0.0)
self.mujoco_simulation.set_qvel("target_rotation", 0.0)
self.mujoco_simulation.set_qvel("target_top_face", 0.0)
self.mujoco_simulation.set_qvel("target_bottom_face", 0.0)
self.mujoco_simulation.forward()
@classmethod
def _get_default_wrappers(cls):
default_wrappers = super()._get_default_wrappers()
default_wrappers.update(
{
"default_observation_noise_levels": {
"fingertip_pos": {"uncorrelated": 0.002, "additive": 0.001},
"hand_angle": {"additive": 0.1, "uncorrelated": 0.1},
"cube_pos": {"additive": 0.005, "uncorrelated": 0.001},
"cube_quat": {"additive": 0.1, "uncorrelated": 0.09},
"cube_face_angle": {"additive": 0.1, "uncorrelated": 0.1},
},
"default_no_noise_levels": {
"fingertip_pos": {},
"hand_angle": {},
"cube_pos": {},
"cube_quat": {},
"cube_face_angle": {},
},
"default_observation_delay_levels": {
"interpolators": {
"cube_quat": "QuatInterpolator",
"cube_face_angle": "RadianInterpolator",
},
"groups": {
# Uncomment below to enable observation delay randomization.
# "vision": {
# "obs_names": ["cube_pos", "cube_quat"],
# "mean": 3,
# "std": 0.5,
# },
# "giiker": {
# "obs_names": ["cube_face_angle"],
# "mean": 1,
# "std": 0.2,
# },
# "phasespace": {
# "obs_names": ["fingertip_pos"],
# "mean": 0.5,
# "std": 0.1,
# }
},
},
"default_no_observation_delay_levels": {
"interpolators": {},
"groups": {},
},
"pre_obsnoise_randomizations": [
["RandomizedActionLatency"],
["RandomizedPerpendicularCubeSizeWrapper"],
["RandomizedBodyInertiaWrapper"],
["RandomizedTimestepWrapper"],
["RandomizedRobotFrictionWrapper"],
["RandomizedCubeFrictionWrapper"],
["RandomizedGravityWrapper"],
["RandomizedWindWrapper"],
["RandomizedPhasespaceFingersWrapper"],
["RandomizedRobotDampingWrapper"],
["RandomizedRobotKpWrapper"],
["RandomizedFaceDampingWrapper"],
["RandomizedJointLimitWrapper"],
["RandomizedTendonRangeWrapper"],
],
}
)
return default_wrappers
make_simple_env = functools.partial(FacePerpendicularEnv.build, apply_wrappers=False)
make_env = FacePerpendicularEnv.build
| 19,795 | 39.154158 | 99 | py |
robogym | robogym-master/robogym/envs/dactyl/locked.py | import functools
import attr
import numpy as np
import robogym.utils.rotation as rotation
from robogym.envs.dactyl.common.cube_env import (
CubeEnv,
CubeSimulationInterface,
CubeSimulationParameters,
DactylCubeEnvConstants,
DactylCubeEnvParameters,
)
from robogym.envs.dactyl.common.mujoco_modifiers import LockedCubeSizeModifier
from robogym.envs.dactyl.goals.locked_parallel import LockedParallelGoal
from robogym.envs.dactyl.observation.cube import (
GoalCubeRotObservation,
GoalIsAchievedObservation,
GoalQposObservation,
MujocoCubePosObservation,
MujocoCubeRotObservation,
)
from robogym.envs.dactyl.observation.locked import GoalCubePosObservation
from robogym.envs.dactyl.observation.shadow_hand import (
MujocoShadowhandAngleObservation,
MujocoShadowhandRelativeFingertipsObservation,
)
from robogym.goal.goal_generator import GoalGenerator
from robogym.mujoco.mujoco_xml import MujocoXML
from robogym.observation.image import ImageObservation
from robogym.observation.mujoco import MujocoQposObservation, MujocoQvelObservation
from robogym.robot_env import ObservationMapValue as omv
from robogym.robot_env import build_nested_attr
@attr.s(auto_attribs=True)
class LockedSimulationParameters(CubeSimulationParameters):
cube_appearance: str = "texture"
@attr.s(auto_attribs=True)
class LockedEnvParameters(DactylCubeEnvParameters):
""" Parameters of the Dactyl Locked env - possible to change for each episode. """
simulation_params: LockedSimulationParameters = build_nested_attr(
LockedSimulationParameters
)
@attr.s(auto_attribs=True)
class LockedEnvConstants(DactylCubeEnvConstants):
""" Parameters of the Dactyl Locked env - same for all episodes. """
# Max number of steps before considering goal is unreachable.
max_steps_goal_unreachable: int = 10
# Threshold for success conditions
success_threshold: dict = {"cube_quat": 0.4}
# Goal generation.
goal_generation: str = "state"
# If use vision base observations.
vision_observations: bool = False
# If use vision base goal observation.
vision_goal: bool = False
class LockedSimulation(CubeSimulationInterface):
"""
Simulation of ShadowHand manipulating the locked cube.
"""
# Perpendicular cube model
CUBE_XML_PATH_PATTERN = "rubik/rubik_locked{suffix}.xml"
@classmethod
def _build_mujoco_cube_xml(cls, xml, cube_xml_path):
xml.append(
MujocoXML.parse(cube_xml_path)
.remove_objects_by_name("annotation:outer_bound")
.add_name_prefix("cube:")
.set_named_objects_attr("cube:middle", tag="body", pos=[1.0, 0.87, 0.2])
.set_named_objects_attr("cube:middle", tag="geom", density=421.0)
)
# Target cube
xml.append(
MujocoXML.parse(cube_xml_path)
.remove_objects_by_name("annotation:outer_bound")
.add_name_prefix("target:")
.set_named_objects_attr("target:middle", tag="body", pos=[1.0, 0.87, 0.2])
# Disable collisions
.set_objects_attr(tag="geom", group="2", conaffinity="0", contype="0")
)
@classmethod
def _get_model_xml_path_and_params(cls, cube_appearance):
if cube_appearance == "texture":
cube_xml_path = cls.CUBE_XML_PATH_PATTERN.format(suffix="")
elif cube_appearance == "material":
cube_xml_path = cls.CUBE_XML_PATH_PATTERN.format(suffix="_material_cells")
elif cube_appearance == "obstacles":
cube_xml_path = cls.CUBE_XML_PATH_PATTERN.format(suffix="_with_obstacles")
elif cube_appearance == "openai":
cube_xml_path = cls.CUBE_XML_PATH_PATTERN.format(suffix="_openai")
else:
raise ValueError(f"Unrecognized cube appearance: {cube_appearance}")
return cube_xml_path, {}
def __init__(self, mujoco_simulation):
super().__init__(mujoco_simulation)
self.register_joint_group("cube_position", prefix="cube:cube_t")
self.register_joint_group("cube_rotation", prefix="cube:cube_rot")
self.register_joint_group("target_position", prefix="target:cube_t")
self.register_joint_group("target_rotation", prefix="target:cube_rot")
self.register_joint_group("target_all_joints", prefix="target:")
self.register_joint_group("hand_angle", prefix="robot0:")
class LockedEnv(CubeEnv[LockedEnvParameters, LockedEnvConstants, LockedSimulation]):
"""
Environment with the ShadowHand and a locked cube (i.e. no moving pieces, just a solid
block).
"""
def _default_observation_map(self):
obs_map = {
"cube_pos": omv({"mujoco": MujocoCubePosObservation}),
"cube_quat": omv({"mujoco": MujocoCubeRotObservation}),
"qpos": omv({"mujoco": MujocoQposObservation}),
"qvel": omv({"mujoco": MujocoQvelObservation}),
"hand_angle": omv({"mujoco": MujocoShadowhandAngleObservation}),
"fingertip_pos": omv(
{"mujoco": MujocoShadowhandRelativeFingertipsObservation}
),
"goal_pos": omv({"goal": GoalCubePosObservation}),
"goal_quat": omv({"goal": GoalCubeRotObservation}),
"qpos_goal": omv({"goal": GoalQposObservation}),
"is_goal_achieved": omv({"goal": GoalIsAchievedObservation}),
}
if self.constants.vision_observations:
# Add image observations for vision based policy rollout.
obs_map.update(
{
"vision": omv(
{"dummy_vision": ImageObservation}, default="dummy_vision"
)
}
)
if self.constants.vision_goal:
obs_map.update(
{
"vision_goal": omv(
{
"goal": ImageObservation,
"goal_render_image": ImageObservation,
"goal_dummy_vision": ImageObservation,
},
default="goal_dummy_vision",
),
}
)
return obs_map
@classmethod
def build_goal_generation(cls, constants, mujoco_simulation) -> GoalGenerator:
""" Construct a goal generation object """
assert (
constants.goal_generation == "state"
), "Only state based goal generation is supported"
return LockedParallelGoal(mujoco_simulation)
@classmethod
def build_simulation(cls, constants, parameters):
return LockedSimulation.build(
n_substeps=constants.mujoco_substeps,
simulation_params=parameters.simulation_params,
)
@classmethod
def build_mujoco_modifiers(cls):
modifiers = super().build_mujoco_modifiers()
modifiers["cube_size_multiplier"] = LockedCubeSizeModifier("cube:")
return modifiers
###############################################################################################
# Internal API
def _randomize_cube_initial_position(self):
""" Draw a random initial position for a cube """
# Original env had this, but I'm not really sure it's needed
for i in range(self.constants.reset_initial_steps):
ctrl = self.mujoco_simulation.shadow_hand.denormalize_position_control(
self.mujoco_simulation.shadow_hand.zero_control()
)
self.mujoco_simulation.shadow_hand.set_position_control(ctrl)
self.mujoco_simulation.step()
cube_translation = (
self._random_state.randn(3) * self.parameters.cube_position_wiggle_std
)
self.mujoco_simulation.add_qpos("cube_position", cube_translation)
cube_orientation = rotation.uniform_quat(self._random_state)
self.mujoco_simulation.set_qpos("cube_rotation", cube_orientation)
# Need to call this after the qpos is modified
self.mujoco_simulation.forward()
action = self._random_state.uniform(-1.0, 1.0, self.action_space.shape[0])
for _ in range(self.parameters.n_random_initial_steps):
ctrl = self.mujoco_simulation.shadow_hand.denormalize_position_control(
action
)
self.mujoco_simulation.shadow_hand.set_position_control(ctrl)
self.mujoco_simulation.step()
@classmethod
def _get_default_wrappers(cls):
default_wrappers = super()._get_default_wrappers()
default_wrappers.update(
{
"default_observation_noise_levels": {
"fingertip_pos": {"uncorrelated": 0.002, "additive": 0.001},
"hand_angle": {"additive": 0.1, "uncorrelated": 0.1},
"cube_pos": {"additive": 0.005, "uncorrelated": 0.001},
"cube_quat": {"additive": 0.1, "uncorrelated": 0.09},
},
"default_no_noise_levels": {
"fingertip_pos": {},
"hand_angle": {},
"cube_pos": {},
"cube_quat": {},
},
"default_observation_delay_levels": {
"interpolators": {"cube_quat": "QuatInterpolator"},
"groups": {
# Uncomment below to enable observation delay randomization.
# "vision": {
# "obs_names": ["cube_pos", "cube_quat"],
# "mean": 3,
# "std": 0.5,
# },
# "phasespace": {
# "obs_names": ["fingertip_pos"],
# "mean": 0.5,
# "std": 0.1,
# }
},
},
"default_no_observation_delay_levels": {
"interpolators": {},
"groups": {},
},
"pre_obsnoise_randomizations": [
["RandomizedActionLatency"],
["RandomizedCubeSizeWrapper"],
["RandomizedBodyInertiaWrapper"],
["RandomizedTimestepWrapper"],
["RandomizedRobotFrictionWrapper"],
["RandomizedCubeFrictionWrapper"],
["RandomizedGravityWrapper"],
["RandomizedWindWrapper"],
["RandomizedPhasespaceFingersWrapper"],
["RandomizedRobotDampingWrapper"],
["RandomizedRobotKpWrapper"],
["RandomizedJointLimitWrapper"],
["RandomizedTendonRangeWrapper"],
],
}
)
return default_wrappers
###############################################################################################
# External API - to establish communication with other parts of the system
@property
def cube_type(self):
""" Type of cube """
return "locked"
###############################################################################################
# Fully internal methods
def _render_callback(self, _sim, _viewer):
""" Set a render callback """
self.mujoco_simulation.set_qpos("target_position", np.array([0.15, 0, -0.03]))
self.mujoco_simulation.set_qpos("target_rotation", self._goal["cube_quat"])
self.mujoco_simulation.set_qvel("target_all_joints", 0.0)
self.mujoco_simulation.forward()
make_simple_env = functools.partial(LockedEnv.build, apply_wrappers=False)
make_env = LockedEnv.build
| 11,793 | 37.542484 | 99 | py |
robogym | robogym-master/robogym/envs/dactyl/common/cube_env.py | import abc
from contextlib import contextmanager
from typing import Dict, List, Optional, Tuple, TypeVar
import attr
import numpy as np
from robogym.envs.dactyl.common import cube_utils
from robogym.envs.dactyl.common.cube_utils import DEFAULT_CAMERA_NAMES
from robogym.envs.dactyl.common.dactyl_cube_wrappers import apply_wrappers
from robogym.mujoco.mujoco_xml import MujocoXML
from robogym.mujoco.simulation_interface import (
SimulationInterface,
SimulationParameters,
)
from robogym.observation.dummy_vision import (
DummyVisionGoalObservationProvider,
DummyVisionObservationProvider,
)
from robogym.observation.goal import GoalObservationProvider
from robogym.observation.mujoco import MujocoObservationProvider, ObservationProvider
from robogym.robot.shadow_hand.mujoco.mujoco_shadow_hand import MuJoCoShadowHand
from robogym.robot_env import (
RobotEnv,
RobotEnvConstants,
RobotEnvParameters,
build_nested_attr,
)
@attr.s(auto_attribs=True)
class CubeSimulationParameters(SimulationParameters):
"""Simulation parameters for cube env."""
# Cube appearance for mujoco and vision rendering.
cube_appearance: str = "policy"
# If True, hide target in simulation.
hide_target: bool = False
@attr.s(auto_attribs=True)
class DactylCubeEnvParameters(RobotEnvParameters):
""" Parameters of the Dactyl cube env - possible to change between episodes """
# Standard deviation of initial cube position
cube_position_wiggle_std: float = 0.005
# Multiplier of the cube size
cube_size_multiplier: float = 1.0
simulation_params: CubeSimulationParameters = build_nested_attr(
CubeSimulationParameters
)
@attr.s(auto_attribs=True)
class DactylCubeEnvConstants(RobotEnvConstants):
""" Parameters of the Dactyl cube env - set once and for all """
# Maximum number of tries to place cube randomly before we give up for given episode
max_pose_resets: int = 50
#####################
# Multi success related settings
# How many successes needed to stop roll out.
successes_needed: int = 50
max_timesteps_per_goal: int = 400
# Check if current goal is reachable in each step. This is disabled by default
# as to get more reliably performance metrics. However, this should be enabled
# for demos or when recording a video.
check_goal_reachable: bool = False
# Max number of steps before considering goal is unreachable.
max_steps_goal_unreachable: int = 100
success_pause_range_s: Tuple[float, float] = (0.0, 0.0)
# Currently supported providers are:
#
# phasespace: Enable phasespace tracking for hand only.
# phasespace/cube: Enable phasespace tracking for both hand and locked cube.
# giiker: Enable giiker.
# vision: Enable vision.
# shadowhand: Enable shadowhand sensor.
#
# Example for observation_configs below.
#
# Locked cube:
# {'cube_pos': 'phasespace', 'cube_quat': 'phasespace', 'fingertip_pos': 'phasespace'}
#
# Full cube:
# {
# 'cube_pos': 'vision',
# 'cube_quat': 'vision',
# 'cube_face_angle': 'giiker',
# 'fingertip_pos': 'phasespace'
# }
#
# How many steps with zero action to take on env reset
reset_initial_steps: int = 20
#####################
# Wrapper settings
# Args for vision wrapper.
vision_args: Optional[dict] = None
# If fix wrist joint position
fixed_wrist: bool = False
# If use relative goal wrapper.
relative_goal_wrapper: bool = True
# Reward for dropping the cube.
drop_reward: float = -20.0
# Minimum episode length.
min_episode_length: int = -1
# The cameras to be used when providing vision observations and/or goals.
camera_names: List[str] = DEFAULT_CAMERA_NAMES
# Width and height of images (only relevant for vision policies).
image_size: int = 200
class CubeSimulationInterface(SimulationInterface):
"""
Simulation interface for shadow hand manipulating some version of a cube.
Should be subclassed to implement particular version of a cube that simulation will manipulate.
"""
# Perpendicular cube model
CUBE_XML = "rubik/rubik_perpendicular.xml"
# XML with slightly different cube model for vision rendering
VISION_CUBE_XML = "rubik/rubik_perpendicular_vision.xml"
# XML for vision rendering with corner cutoff for center piece.
VISION_CUBE_CORNER_CUTOFF_XML = "rubik/rubik_perpendicular_vision_corner_cutoff.xml"
# Just a floor
FLOOR_XML = "floor/basic_floor.xml"
# Robot hand xml
HAND_XML = "robot/shadowhand/main.xml"
# XML with default light
LIGHT_XML = "light/default.xml"
# XML with default camera
CAMERA_XML = "camera/default.xml"
def __init__(self, sim):
super().__init__(sim)
self.enable_pid()
self.shadow_hand = MuJoCoShadowHand(self)
def is_cube_on_palm(self):
""" Determines if cube is on palm """
return cube_utils.on_palm(self.sim)
@classmethod
def _build_mujoco_cube_xml(cls, xml, cube_xml_path):
""" Builc xml for the cube simulation """
raise NotImplementedError
@classmethod
def build(
cls,
n_substeps: int = 10,
simulation_params: CubeSimulationParameters = CubeSimulationParameters(),
):
"""
Construct a CubeSimulationInterface object with perpendicular cube.
"""
cube_xml_path, size_params = cls._get_model_xml_path_and_params(
simulation_params.cube_appearance
)
xml = MujocoXML()
xml.add_default_compiler_directive()
cls._build_mujoco_cube_xml(xml, cube_xml_path)
xml.append(
MujocoXML.parse(cls.FLOOR_XML).set_named_objects_attr(
"floor", tag="body", pos=[1, 1, 0]
)
)
xml.append(
MujocoXML.parse(cls.HAND_XML)
.add_name_prefix("robot0:")
.set_objects_attr(tag="size", **size_params)
.set_named_objects_attr(
"robot0:hand_mount",
tag="body",
pos=[1.0, 1.25, 0.15],
euler=[np.pi / 2, 0, np.pi],
)
.remove_objects_by_name("robot0:annotation:outer_bound")
# Remove hand base free joint so that hand is immovable
.remove_objects_by_name("robot0:hand_base")
)
xml.append(MujocoXML.parse(cls.LIGHT_XML))
simulation = cls(xml.build(nsubsteps=n_substeps))
if simulation_params.hide_target:
simulation.hide_target()
return simulation
@classmethod
def _get_model_xml_path_and_params(cls, cube_appearance):
assert cube_appearance in (
"policy",
"vision",
"vision_corner_cutoff",
), f"Unexpected cube type: {cube_appearance}"
if cube_appearance == "vision":
cube_xml_path = cls.VISION_CUBE_XML
max_contacts_params = dict(
njmax=6000, nconmax=600, nuserdata=100, nuser_actuator=20
)
elif cube_appearance == "vision_corner_cutoff":
cube_xml_path = cls.VISION_CUBE_CORNER_CUTOFF_XML
max_contacts_params = dict(
njmax=6000, nconmax=600, nuserdata=100, nuser_actuator=20
)
else:
cube_xml_path = cls.CUBE_XML
max_contacts_params = dict(
njmax=2000, nconmax=200, nuserdata=100, nuser_actuator=20
)
return cube_xml_path, max_contacts_params
@contextmanager
def hide_target(self):
""" Make target transparent (or invisible if we want to hide_target) """
target_geom_ids = [
self.sim.model.geom_name2id(name)
for name in self.sim.model.geom_names
if name.startswith("target")
]
target_mat_ids = [self.sim.model.geom_matid[gid] for gid in target_geom_ids]
target_site_ids = [
self.sim.model.site_name2id(name)
for name in self.sim.model.site_names
if name.startswith("target")
]
old_mat_rgba = self.sim.model.mat_rgba.copy()
old_geom_rgba = self.sim.model.geom_rgba.copy()
old_site_rgba = self.sim.model.site_rgba.copy()
self.sim.model.mat_rgba[target_mat_ids, -1] = 0
self.sim.model.geom_rgba[target_geom_ids, -1] = 0
self.sim.model.site_rgba[target_site_ids, -1] = 0
yield
# Make target visible again.
self.sim.model.mat_rgba[:] = old_mat_rgba
self.sim.model.geom_rgba[:] = old_geom_rgba
self.sim.model.site_rgba[:] = old_site_rgba
PType = TypeVar("PType", bound=DactylCubeEnvParameters)
CType = TypeVar("CType", bound=DactylCubeEnvConstants)
SType = TypeVar("SType", bound=CubeSimulationInterface)
class CubeEnv(RobotEnv[PType, CType, SType], abc.ABC):
"""
Base class for dactyl cube environments.
Locked, Face, Full, Perpendicular, with right subclass should handle all of them
"""
def _build_observation_providers(self):
"""
Initialize observation providers for the environment.
"""
providers: Dict[str, ObservationProvider] = {
"mujoco": MujocoObservationProvider(self.mujoco_simulation),
"goal": GoalObservationProvider(self.goal_info),
}
if "dummy_vision" in self.constants.observation_providers:
providers["dummy_vision"] = DummyVisionObservationProvider(
camera_names=self.constants.camera_names,
image_size=self.constants.image_size,
)
providers["goal_dummy_vision"] = DummyVisionGoalObservationProvider(
get_goal=self.goal_info,
goal_qpos_key="qpos_goal",
camera_names=self.constants.camera_names,
image_size=self.constants.image_size,
)
return providers
def _has_episode_ended(self):
""" Check if simulation is in state good enough to continue training """
return not self.mujoco_simulation.is_cube_on_palm()
def _setup_simulation_from_parameters(self):
"""
Set all the simulation parameters from the current settings.
You may override it or just leave it as it is for a very basic setup.
"""
for param_name, modifier in self.modifiers:
modifier(getattr(self.parameters, param_name))
###############################################################################################
# Internal API - to be overridden - environment randomization
def _randomize_cube_initial_position(self):
""" Draw a random initial position for a cube """
raise NotImplementedError("Override _randomize_cube_initial_position")
def _reset(self):
"""Resets the state of the environment and returns an initial observation.
Returns: observation (object): the initial observation of the
space.
"""
# Basically randomize cube position, until we get some random state where cube is still on
# the palm of the hand
for _ in range(self.constants.max_pose_resets):
# Reset accumulated warnings
self.warning_buffer.clear()
# Reset to the initial state
self.mujoco_simulation.reset()
# Set all the simulation parameters
self._setup_simulation_from_parameters()
# Set derived constants of the simulation
self.mujoco_simulation.set_constants()
# Randomize cube position.
self._randomize_cube_initial_position()
if not self._has_episode_ended():
break
@classmethod
def build_robot(cls, mujoco_simulation, physical):
return mujoco_simulation.shadow_hand
def apply_wrappers(self, **wrapper_params):
"""
Apply wrappers to the environment.
"""
self.constants: DactylCubeEnvConstants
return apply_wrappers(
self,
randomize=self.constants.randomize,
n_action_bins=self.constants.n_action_bins,
fixed_wrist=self.constants.fixed_wrist,
relative_goal_wrapper=self.constants.relative_goal_wrapper,
drop_reward=self.constants.drop_reward,
default_wrappers=self._get_default_wrappers(),
min_episode_length=self.constants.min_episode_length,
**wrapper_params,
)
@classmethod
def _get_default_wrappers(cls):
return {
"post_obsnoise_randomizations": [
["FingersOccludedPhasespaceMarkers"],
["FingersFreezingPhasespaceMarkers"],
["CubeFreezingPhasespaceBody"],
["ActionNoiseWrapper"],
],
}
| 12,925 | 32.228792 | 99 | py |
robogym | robogym-master/robogym/envs/dactyl/common/cube_manipulator.py | import collections
from typing import Dict, Tuple
import numpy as np
import pycuber
from robogym.utils import rotation
PYCUBER_LOCATION_AXES: Dict[str, np.array] = {
"L": np.array([-1, 0, 0]),
"R": np.array([1, 0, 0]),
"F": np.array([0, -1, 0]),
"B": np.array([0, 1, 0]),
"D": np.array([0, 0, -1]),
"U": np.array([0, 0, 1]),
}
PYCUBER_COLOR_AXES: Dict[str, np.array] = {
"red": np.array([-1, 0, 0]),
"orange": np.array([1, 0, 0]),
"blue": np.array([0, 1, 0]),
"green": np.array([0, -1, 0]),
"yellow": np.array([0, 0, 1]),
"white": np.array([0, 0, -1]),
}
# Below mappings are represented as pairs (axis nr, sign)
# e.g. (0, -1) means -X and (2, 1) means +Z
PYCUBER_COLOR_AXES_DESCRIPTIONS: Dict[str, Tuple] = {
"red": (0, -1),
"orange": (0, 1),
"blue": (1, 1),
"green": (1, -1),
"yellow": (2, 1),
"white": (2, -1),
}
PYCUBER_REVERSE_LOCATIONS: Dict[Tuple, str] = {
(0, -1): "L",
(0, 1): "R",
(1, -1): "F",
(1, 1): "B",
(2, -1): "D",
(2, 1): "U",
}
PYCUBER_REVERSE_COLORS: Dict[Tuple, str] = {
(0, -1): "red",
(0, 1): "orange",
(1, 1): "blue",
(1, -1): "green",
(2, 1): "yellow",
(2, -1): "white",
}
class CubeManipulator:
"""
Class for manipulating the perpendicular rubik's cube model in mujoco simulation.
Translates face rotation commands into angle representation that can be input into
mujoco qpos array
"""
def __init__(self, prefix, sim):
self.prefix = prefix
self.sim = sim
self.drivers = [
x
for x in self.sim.model.joint_names
if x.startswith("{}cubelet:driver:".format(self.prefix))
]
self.rotators = [
x
for x in self.sim.model.joint_names
if x.startswith("{}cubelet:rot".format(self.prefix))
]
self.joints = self.drivers + self.rotators
self.joints_qpos_map = {}
self.joints_qpos_idx = []
for j in self.joints:
current_index = self.sim.model.get_joint_qpos_addr(j)
self.joints_qpos_map[j] = current_index
self.joints_qpos_idx.append(current_index)
# Info about the cubelets - indexed by cubelet index
self.cubelet_meta_info = []
# Populate cubelet meta information
for i in range(3):
for j in range(3):
for k in range(3):
indicators = collections.OrderedDict(
[("x", i - 1), ("y", j - 1), ("z", k - 1)]
)
# Build cubelet nam
name_pieces = []
keys = []
for key, value in indicators.items():
if value == -1:
keys.append(key)
name_pieces.append("neg_{}".format(key))
elif value == 1:
keys.append(key)
name_pieces.append("pos_{}".format(key))
name = "_".join(name_pieces)
data = {"name": name, "coords": np.array([i - 1, j - 1, k - 1])}
if len(name_pieces) > 1:
# Not a driver just a normal cubelet
idxs = [
self.joints.index(
"{}cubelet:rot{}:{}".format(prefix, key, name)
)
for key in indicators
]
data["type"] = "cubelet"
data["euler_qpos"] = [self.joints_qpos_idx[i] for i in idxs]
elif len(name_pieces) == 1:
data["driver"] = self.drivers.index(
"{}cubelet:driver:{}".format(prefix, name)
)
data["type"] = "driver"
else:
data["type"] = "null"
self.cubelet_meta_info.append(data)
def _cubelet_rotation_matrix(self, cubelet_meta_info, qpos_array):
""" Find local coordinate axes for the cubelet """
euler_angles = qpos_array[cubelet_meta_info["euler_qpos"]]
return rotation.euler2mat(euler_angles)
def rotate_face(self, axis, side, angle):
"""
Rotate given face (identified by axis and side) by given angle.
Cube should be in a reasonably aligned state for this to work well.
"""
assert 0 <= axis <= 2
assert 0 <= side <= 1
angle = rotation.normalize_angles(np.array(angle))
if np.abs(angle) < 1e-4:
# No need to do anything, the angle is too small to care
return
side = side * 2 - 1
qpos_copy = self.sim.data.qpos.copy()
# For each cubelet
for i in range(27):
cubelet_meta = self.cubelet_meta_info[i]
if cubelet_meta["type"] == "cubelet":
mtx = self._cubelet_rotation_matrix(cubelet_meta, qpos_copy)
current_coords = mtx @ cubelet_meta["coords"].astype(float)
is_selected = np.take(current_coords, axis) * side > 0.5
if is_selected:
euler = np.zeros(3)
euler[axis] = angle
combined_matrix = rotation.euler2mat(euler) @ mtx
new_euler = rotation.mat2euler(combined_matrix)
self.sim.data.qpos[cubelet_meta["euler_qpos"]] = new_euler
elif cubelet_meta["type"] == "driver":
# No transformation matrix really here
current_coords = cubelet_meta["coords"]
is_selected = np.take(current_coords, axis) * side > 0.5
if is_selected:
joint_idx = self.joints_qpos_idx[cubelet_meta["driver"]]
self.sim.data.qpos[joint_idx] += angle
def from_pycuber(self, cube: pycuber.Cube):
"""
Set cubelet positions based on the pycuber cube state
Image copied from rubik_utils.py
Z(+) Up (Yellow) Faces:
| +X: Right (Orange)
| / Y(+) Back (Blue) -X: Left (Red)
_____________ / +Y: Back (Blue)
/ /| -Y: Front (Green)
/ / | +Z: Up (Yellow)
/ / | -Z: Down (White)
/____________/ |
| | |____ X(+) Right (Orange)
Left | | /
(Red) | Front | /
| (Green) | /
|____________|/
Down (White)
"""
# First, we zero out the cubelet positions to reset all of the cube state
self.sim.data.qpos[self.joints_qpos_idx] = 0.0
for cubelet in cube.children:
if isinstance(cubelet, pycuber.cube.Corner):
mtx = np.zeros((3, 3))
for element in cubelet.location:
# Example: Corner(B: [r], U: [y], L: [g])
# Original location: red, yellow, green: -X, +Z, -Y
# Current location back, up, left: +Y, +Z, -X
# Mapping: -X -> +Y, +Z -> +Z, -Y -> -X
axis, sign = PYCUBER_COLOR_AXES_DESCRIPTIONS[
cubelet[element].colour
]
vector = PYCUBER_LOCATION_AXES[element]
mtx[:, axis] = sign * vector
euler_angles = rotation.mat2euler(mtx)
original_location: np.array = sum(
PYCUBER_COLOR_AXES[x.colour] for x in cubelet.children
)
idx = (
(original_location[0] + 1) * 9
+ (original_location[1] + 1) * 3
+ original_location[2]
+ 1
)
# Set the euler angles
self.sim.data.qpos[
self.cubelet_meta_info[idx]["euler_qpos"]
] = euler_angles
elif isinstance(cubelet, pycuber.cube.Edge):
# Example:
# Edge(R: [o], B: [g])
# original location: orange-green +X, -Y, (1, -1, 0)
# current location: right-back, +X, +Y (1, 1, 0)
original_location = sum(
PYCUBER_COLOR_AXES[x.colour] for x in cubelet.children
)
mtx = np.zeros((3, 3))
axes = {0, 1, 2}
for element in cubelet.location:
axis, sign = PYCUBER_COLOR_AXES_DESCRIPTIONS[
cubelet[element].colour
]
vector = PYCUBER_LOCATION_AXES[element]
mtx[:, axis] = sign * vector
axes.remove(axis)
remaining_axis = axes.pop()
# Antisymmetric tensor
if remaining_axis == 0:
mtx[:, 0] = np.cross(mtx[:, 1], mtx[:, 2])
elif remaining_axis == 1:
mtx[:, 1] = -np.cross(mtx[:, 0], mtx[:, 2])
elif remaining_axis == 2:
mtx[:, 2] = np.cross(mtx[:, 0], mtx[:, 1])
euler_angles = rotation.mat2euler(mtx)
idx = (
(original_location[0] + 1) * 9
+ (original_location[1] + 1) * 3
+ original_location[2]
+ 1
)
# Set the euler angles
self.sim.data.qpos[
self.cubelet_meta_info[idx]["euler_qpos"]
] = euler_angles
def to_pycuber(self) -> pycuber.Cube:
""" Return current cubelet state as a pycuber state """
self.soft_align_faces()
qpos_copy = self.sim.data.qpos.copy()
cubies = []
for i in range(27):
cubelet_meta = self.cubelet_meta_info[i]
if cubelet_meta["type"] == "cubelet":
mtx = self._cubelet_rotation_matrix(cubelet_meta, qpos_copy)
original_coords = cubelet_meta["coords"]
# current_coords = (mtx @ cubelet_meta['coords'].astype(float)).round().astype(int)
cubie_desc = {}
for prev_axis, sign in enumerate(original_coords):
if sign != 0:
vec = mtx[:, prev_axis] * sign
new_axis = np.abs(vec).argmax()
new_sign = vec[new_axis]
color = PYCUBER_REVERSE_COLORS[prev_axis, sign]
loc = PYCUBER_REVERSE_LOCATIONS[new_axis, new_sign]
cubie_desc[loc] = pycuber.Square(color)
if len(cubie_desc) == 3:
cubies.append(pycuber.Corner(**cubie_desc))
elif len(cubie_desc) == 2:
cubies.append(pycuber.Edge(**cubie_desc))
if cubelet_meta["type"] == "driver":
original_coords = cubelet_meta["coords"]
axis = np.abs(original_coords).argmax()
sign = original_coords[axis]
color = PYCUBER_REVERSE_COLORS[axis, sign]
loc = PYCUBER_REVERSE_LOCATIONS[axis, sign]
cubie_desc = {loc: pycuber.Square(color)}
cubies.append(pycuber.Centre(**cubie_desc))
return pycuber.Cube(cubies=cubies)
def snap_rotate_face_with_threshold(self, axis, side, angle, threshold=0.1):
"""
Rotate face of a cube in a "snapping" fashion, correcting the cube along the way.
Underlying assumption: cube is already in a "snapped", physically-aligned state
Threshold is threshold in radians which decides maximum angle we want to snap over.
If the angle required to move the face to be snapped is larger than that, the cube
will remain locked and won't rotate.
"""
qpos = self.sim.data.qpos
drivers = rotation.normalize_angles(
qpos[[self.joints_qpos_map[x] for x in self.drivers]]
)
perpendicular_axes = sorted({0, 1, 2} - {axis})
transaction = []
abort = False
for other_axis in perpendicular_axes:
for other_side in range(2):
other_driver_idx = other_axis * 2 + other_side
other_angle = drivers[other_driver_idx]
other_angle_aligned = rotation.round_to_straight_angles(other_angle)
other_angle_diff = rotation.normalize_angles(
other_angle_aligned - other_angle
)
if (
np.abs(other_angle_diff) < np.abs(angle)
and np.abs(other_angle_diff) < threshold
):
transaction.append((other_axis, other_side, other_angle_diff))
else:
abort = True
if not abort:
# Snap other faces
for other_axis, other_side, angle_diff in transaction:
self.rotate_face(other_axis, other_side, angle_diff)
# rotate the actual face
self.rotate_face(axis, side, angle)
def soft_align_faces(self):
"""
Align cube configuration to nearest set of straight angles.
Should handle more corner cases than naive implementation
"""
drivers_idx = [self.joints_qpos_map[x] for x in self.drivers]
current_angles = self.sim.data.qpos[drivers_idx]
straight_angles = rotation.round_to_straight_angles(current_angles)
normalized_diff = rotation.normalize_angles(straight_angles - current_angles)
# From the largest angle to the smallest
for _, idx in reversed(
sorted(zip(np.abs(normalized_diff), range(len(normalized_diff))))
):
self.rotate_face(idx // 2, idx % 2, normalized_diff[idx])
# Align all little cubelets at the end
for i in range(27):
info = self.cubelet_meta_info[i]
if "euler_qpos" in info:
mtx = self._cubelet_rotation_matrix(info, self.sim.data.qpos)
# Much better alignment than in the euler angle representation
# If the cube is close enough to the aligned state it should work
mtx = mtx.round()
self.sim.data.qpos[info["euler_qpos"]] = rotation.mat2euler(mtx)
def align_angles(self):
"""
Round all cube angles to the nearest straight angle
Naive implementation that may easily cause the cube to end up in an incorrect state
due to "gimbal lock" singularity in the euler angle representation
"""
self.sim.data.qpos[self.joints_qpos_idx] = rotation.round_to_straight_angles(
self.sim.data.qpos[self.joints_qpos_idx]
)
| 15,260 | 34.992925 | 99 | py |
robogym | robogym-master/robogym/envs/dactyl/common/cube_utils.py | import math
import numpy as np
from robogym.mujoco.helpers import joint_qpos_ids_from_prefix
from robogym.utils import rotation
PARALLEL_QUATS = [
rotation.quat_normalize(rotation.euler2quat(r))
for r in rotation.get_parallel_rotations()
]
DEFAULT_CAMERA_NAMES = ["vision_cam_top", "vision_cam_right", "vision_cam_left"]
def on_palm(sim):
""" Determines if the cube is on the palm of the hand."""
sim.forward()
cube_middle_idx = sim.model.site_name2id("cube:center")
cube_middle_pos = sim.data.site_xpos[cube_middle_idx]
is_on_palm = cube_middle_pos[2] > 0.04
return is_on_palm
def uniform_z_aligned_quat(random):
""" Produces a random quaternion with the red face on top. """
axis = np.asarray([0.0, 0.0, 1.0])
angle = random.uniform(-np.pi, np.pi)
quat = rotation.quat_from_angle_and_axis(angle, axis)
return rotation.quat_normalize(quat)
def face_up(sim, geom_names):
""" Return the index of the face which is oriented up. """
face_geom_z = [sim.data.get_geom_xpos(name)[2] for name in geom_names]
return np.argmax(face_geom_z)
def face_up_quats(sim, ball_joint, geom_names):
""" Returns a dict of parallel quats in which the given faces are up. """
goal_quat = {}
cube_quat_idxs = joint_qpos_ids_from_prefix(sim.model, ball_joint)
initial_quat = sim.data.qpos[cube_quat_idxs]
sim.data.qpos[cube_quat_idxs] = rotation.quat_identity()
for i, geom in enumerate(geom_names):
geom_z = []
for p in PARALLEL_QUATS:
sim.data.qpos[cube_quat_idxs] = p
sim.forward()
geom_z.append(sim.data.get_geom_xpos(geom)[2])
goal_quat[i] = PARALLEL_QUATS[np.argmax(geom_z)]
assert len(goal_quat.keys()) == len(geom_names)
sim.data.qpos[cube_quat_idxs] = initial_quat
sim.forward()
return goal_quat
def rotated_face(
face_angles, face_to_shift, random, round_target_face, directions=["cw", "ccw"]
):
"""
Return a new set of face angles, which correspond to the original but with the given
face rotated.
"""
clockwise = math.pow(-1, face_to_shift)
rotation_directions = {
"cw": clockwise,
"ccw": -clockwise,
}
directions = [math.pi / 2 * rotation_directions[d] for d in directions]
rotated_face = face_angles.copy()
if random.uniform() < float(round_target_face):
rotated_face[face_to_shift] += random.choice(directions)
rotated_face = rotation.normalize_angles(rotated_face)
rotated_face = rotation.round_to_straight_angles(rotated_face)
else:
directions += [0.0]
rotated_face[face_to_shift] += random.uniform(min(directions), max(directions))
rotated_face = rotation.normalize_angles(rotated_face)
return rotated_face
def rotated_face_with_angle(
face_angles, face_to_shift, random, round_target_face, directions=["cw", "ccw"]
):
"""
Return a new set of face angles, which correspond to the original but with the given
face rotated.
:param face_angles: Numpy array of current angles of cube faces
:param face_to_shift: Index of the face that we are about to rotate
:param random: Random state used to sample pseudorandom numbers
:param round_target_face: Boolean of floating point probability if the face should be rotated
by 90 degrees or by any uniform angle within range
:param directions: Specify which direction rotations are allowed, supported values are
'cw' and 'ccw'
"""
clockwise = math.pow(-1, face_to_shift)
rotation_directions = {
"cw": clockwise,
"ccw": -clockwise,
}
directions = [math.pi / 2 * rotation_directions[d] for d in directions]
rotated_face = face_angles.copy()
if random.uniform() < float(round_target_face):
rotation_angle = random.choice(directions)
rotated_face[face_to_shift] += rotation_angle
rotated_face = rotation.normalize_angles(rotated_face)
rotated_face = rotation.round_to_straight_angles(rotated_face)
else:
directions += [0.0]
rotation_angle = random.uniform(min(directions), max(directions))
rotated_face[face_to_shift] += rotation_angle
rotated_face = rotation.normalize_angles(rotated_face)
return rotated_face, rotation_angle
def align_quat_up(cube_quat, normalize=True):
""" Align quaternion so that the closest face to being up is actually up """
z_up = np.array([0, 0, 1]).reshape(3, 1)
mtx = rotation.quat2mat(cube_quat)
# Axis that is the closest (by dotproduct) to z-up
axis_nr = np.abs((z_up.T @ mtx)).argmax()
# Axis of the cube pointing the closest to the top
axis = mtx[:, axis_nr]
axis = axis * np.sign(axis @ z_up)
# Quaternion representing the rotation from "axis" that is almost up to
# the actual "up" direction
difference_quat = rotation.vectors2quat(axis, z_up[:, 0])
angle = rotation.quat_mul(difference_quat, cube_quat)
return rotation.quat_normalize(angle) if normalize else angle
def up_axis_with_sign(cube_quat):
""" Return an axis number + sign of the cube that is the closest to pointing up """
z_up = np.array([0, 0, 1]).reshape(3, 1)
mtx = rotation.quat2mat(cube_quat)
# Axis that is the closest (by dotproduct) to z-up
axis_nr = np.abs((z_up.T @ mtx)).argmax()
axis = mtx[:, axis_nr]
sign = np.sign(axis @ z_up)
return axis_nr, sign
def distance_quat_from_being_up(cube_quat, axis_nr, sign):
""" How far is the cube from having given axis pointing upwards """
mtx = rotation.quat2mat(cube_quat)
axis = mtx[:, axis_nr]
axis = axis * sign
z_up = np.array([0, 0, 1]).reshape(3, 1)
# Quaternion representing the rotation from "axis" that is almost up to
# the actual "up" direction
difference_quat = rotation.vectors2quat(axis, z_up[:, 0])
return rotation.quat_normalize(difference_quat)
| 5,962 | 31.763736 | 97 | py |
robogym | robogym-master/robogym/envs/dactyl/common/dactyl_cube_wrappers.py | import logging
from robogym.wrappers.named_wrappers import apply_named_wrappers, edit_wrappers
logger = logging.getLogger(__name__)
def construct_default_wrappers(
*,
randomize: bool,
n_action_bins: int,
fixed_wrist: bool,
adr_wrapper,
relative_goal_wrapper: bool = False,
drop_reward: float = -20.0,
default_wrappers=None,
min_episode_length: int = -1
):
"""
Construct default list of wrappers.
Args:
- randomize (bool): use randomizations. See default-wrapper-base.jsonnet for default
randomization blocks
- vision_args (dict): see base-vision.jsonnet for examples
- n_action_bins (int or None=DiscretizeActionWrapper.DEFAULT_BINS): number of discrete bins
- min_episode_length: If positive, a dropped cube at a timestep below min_epsiode_length will
not trigger a 'done'. A penalty for cube dropping is only returned on the first frame.
Returns: list of wrappers
"""
wrappers = []
# actions should be clipped immediately before sending to env
if fixed_wrist:
wrappers.append(["FixedWristWrapper"]) # must be inside clipping
wrappers.append(["ClipActionWrapper"])
wrappers.append(
[
"StopOnFallWrapper",
dict(min_episode_length=min_episode_length, drop_reward=drop_reward,),
]
)
if randomize:
wrappers.append(["BacklashWrapper"])
if adr_wrapper is not None:
wrappers.append(adr_wrapper)
wrappers += default_wrappers["pre_obsnoise_randomizations"]
noise_levels = default_wrappers["default_observation_noise_levels"]
observation_delay_levels = default_wrappers["default_observation_delay_levels"]
else:
noise_levels = default_wrappers["default_no_noise_levels"]
observation_delay_levels = default_wrappers[
"default_no_observation_delay_levels"
]
wrappers.append(["ObservationDelayWrapper", dict(levels=observation_delay_levels)])
wrappers.append(
["RandomizeObservationWrapper", dict(levels=noise_levels)]
) # must happen before angle observation wrapper
wrappers.append(
["SmoothActionWrapper"]
) # it is important that this gets applied before noise is added
if relative_goal_wrapper:
wrappers.append(["RelativeGoalWrapper", dict(obs_prefix="cube_")])
if randomize:
wrappers += default_wrappers["post_obsnoise_randomizations"]
wrappers.append(["AngleObservationWrapper"])
wrappers.append(
[
"UnifiedGoalObservationWrapper",
dict(goal_parts=["pos", "quat", "face_angle"]),
]
)
wrappers.append(["ClipObservationWrapper"])
wrappers.append(["ClipRewardWrapper"])
wrappers.append(["PreviousActionObservationWrapper"])
wrappers.append(["RewardObservationWrapper", {"reward_inds": [1, 2]}])
wrappers.append(["DiscretizeActionWrapper", {"n_action_bins": n_action_bins}])
return wrappers
def apply_wrappers(
env,
randomize,
wrappers=None,
n_action_bins=None,
fixed_wrist=False,
insert_above=[],
insert_below=[],
replace=[],
delete=[],
adr_wrapper=None,
relative_goal_wrapper=False,
drop_reward=-20.0,
default_wrappers=None,
min_episode_length=-1,
):
if wrappers is None:
wrappers = construct_default_wrappers(
randomize=randomize,
n_action_bins=n_action_bins,
fixed_wrist=fixed_wrist,
drop_reward=drop_reward,
adr_wrapper=adr_wrapper,
relative_goal_wrapper=relative_goal_wrapper,
default_wrappers=default_wrappers,
min_episode_length=min_episode_length,
)
wrappers = edit_wrappers(
wrappers=wrappers,
insert_above=insert_above,
insert_below=insert_below,
replace=replace,
delete=delete,
)
env = apply_named_wrappers(env, wrappers)
return env
def get_vision_wrapper_args(input_vision_args, cube_type):
vision_args = (input_vision_args or {}).copy()
if "vision_env_args" not in vision_args:
vision_args["vision_env_args"] = {}
if cube_type == "full-perpendicular":
vision_env_args = {
"hide_target": True,
"cube_appearance": "vision",
}
elif cube_type in "face-perpendicular":
vision_env_args = {
"hide_target": True,
"randomize": False,
"n_random_initial_steps": 0,
}
elif cube_type == "locked":
vision_env_args = {
"hide_target": True,
"cube_appearance": "material",
"randomize": False,
"n_random_initial_steps": 0,
}
else:
vision_env_args = {}
vision_args["vision_env_args"].update(vision_env_args)
return vision_args
| 4,867 | 29.049383 | 97 | py |
robogym | robogym-master/robogym/envs/dactyl/common/mujoco_modifiers.py | import numpy as np
# noinspection PyUnresolvedReferences
from robogym.mujoco.modifiers.timestep import Modifier
# noinspection PyAttributeOutsideInit
class PerpendicularCubeSizeModifier(Modifier):
""" Modify size of a "perpendicular cube" """
def __init__(self, prefix):
super().__init__()
self.body_name_prefix = f"{prefix}cubelet:"
self.mesh_name = f"{prefix}rounded_cube"
def initialize(self, sim):
super().initialize(sim)
cubelet_body_names = [
x for x in self.sim.model.body_names if x.startswith(self.body_name_prefix)
]
self.cubelet_body_ids = np.array(
[self.sim.model.body_name2id(x) for x in cubelet_body_names]
)
cubelet_geom_names = [
x for x in self.sim.model.geom_names if x.startswith(self.body_name_prefix)
]
self.cubelet_geom_ids = np.array(
[self.sim.model.geom_name2id(x) for x in cubelet_geom_names]
)
cube_mesh_id = self.sim.model.mesh_name2id(self.mesh_name)
self.cube_vert_adr = self.sim.model.mesh_vertadr[cube_mesh_id]
self.cube_vert_num = self.sim.model.mesh_vertnum[cube_mesh_id]
self.original_cube_body_pos = self.sim.model.body_pos[
self.cubelet_body_ids
].copy()
self.original_mesh_verts = (
self.sim.model.mesh_vert[
self.cube_vert_adr: self.cube_vert_adr + self.cube_vert_num
]
).copy()
self.original_geom_rbound = self.sim.model.geom_rbound[
self.cubelet_geom_ids
].copy()
def __call__(self, cube_size_multiplier):
self.sim.model.body_pos[self.cubelet_body_ids] = (
self.original_cube_body_pos * cube_size_multiplier
)
self.sim.model.mesh_vert[
self.cube_vert_adr: self.cube_vert_adr + self.cube_vert_num
] = (self.original_mesh_verts * cube_size_multiplier)
self.sim.model.geom_rbound[self.cubelet_geom_ids] = (
self.original_geom_rbound * cube_size_multiplier
)
# noinspection PyAttributeOutsideInit
class LockedCubeSizeModifier(Modifier):
""" Modify size of a "locked cube" """
def __init__(self, prefix):
super().__init__()
self.prefix = prefix
def initialize(self, sim):
super().initialize(sim)
self.body_ids = [
self.sim.model.body_name2id(x)
for x in self.sim.model.body_names
if x.startswith(self.prefix)
]
self.original_body_pos = self.sim.model.body_pos[self.body_ids].copy()
self.geom_ids = [
self.sim.model.geom_name2id(x)
for x in self.sim.model.geom_names
if x.startswith(self.prefix)
]
self.original_geom_size = self.sim.model.geom_size[self.geom_ids].copy()
def __call__(self, cube_size_multiplier):
self.sim.model.body_pos[self.body_ids] = (
self.original_body_pos * cube_size_multiplier
)
self.sim.model.geom_size[self.geom_ids] = (
self.original_geom_size * cube_size_multiplier
)
| 3,155 | 29.941176 | 87 | py |
robogym | robogym-master/robogym/envs/dactyl/observation/cube.py | import numpy as np
from robogym.observation.goal import GoalObservation
from robogym.observation.mujoco import MujocoObservation
from robogym.utils.rotation import quat_normalize
class MujocoCubePosObservation(MujocoObservation):
"""
Implement mujoco base cube position observation.
"""
def get(self) -> np.ndarray:
"""
Get cube position.
"""
return self.provider.mujoco_simulation.get_qpos("cube_position")
class MujocoCubeRotObservation(MujocoObservation):
"""
Implement mujoco base cube rotation observation.
"""
def get(self) -> np.ndarray:
"""
Get cube position.
"""
return quat_normalize(self.provider.mujoco_simulation.get_qpos("cube_rotation"))
class GoalCubeRotObservation(GoalObservation):
"""
Implement goal cube rotation observation.
"""
def get(self) -> np.ndarray:
"""
Get cube position.
"""
assert self.provider.goal
return quat_normalize(self.provider.goal["cube_quat"])
class GoalQposObservation(GoalObservation):
"""
Retrieves the qpos corresponding to the goal state.
"""
def get(self) -> np.ndarray:
"""
Get goal qpos.
"""
assert self.provider.goal
return self.provider.goal["qpos_goal"]
class GoalIsAchievedObservation(GoalObservation):
"""
Implement observation indicating if we've achieved the current goal.
"""
def get(self) -> np.ndarray:
"""
Get the flag indicating if we've achieved the current goal.
"""
return self.provider.is_successful
| 1,639 | 23.117647 | 88 | py |
robogym | robogym-master/robogym/envs/dactyl/observation/shadow_hand.py | import abc
import numpy as np
from robogym.observation.mujoco import MujocoObservation
from robogym.robot.shadow_hand.hand_forward_kinematics import FINGERTIP_SITE_NAMES
from robogym.robot.shadow_hand.hand_interface import JOINTS
from robogym.robot.shadow_hand.mujoco.mujoco_shadow_hand import MuJoCoShadowHand
def _update_qpos_and_qvel(sim, qpos=None, qvel=None):
for i, joint_name in enumerate(JOINTS):
name = "robot0:" + joint_name
if name in sim.model.joint_names:
if qpos is not None:
sim.data.qpos[sim.model.get_joint_qpos_addr(name)] = qpos[i]
if qvel is not None:
sim.data.qvel[sim.model.get_joint_qvel_addr(name)] = qvel[i]
class MujocoShadowHandObservation(MujocoObservation, abc.ABC):
def __init__(self, provider):
super().__init__(provider)
self.hand = MuJoCoShadowHand(self.provider.mujoco_simulation)
class MujocoShadowhandRelativeFingertipsObservation(MujocoShadowHandObservation):
"""
Mujoco based relative fingertip position observation.
"""
def get(self) -> np.ndarray:
"""
Get relative fingertip positions.
"""
return self.hand.observe().fingertip_positions().flatten()
class MujocoShadowhandAbsoluteFingertipsObservation(MujocoShadowHandObservation):
"""
Mujoco based absolute fingertip position observation.
"""
def get(self) -> np.ndarray:
"""
Get relative fingertip positions.
"""
fingertip_pos = np.array(
[
self.provider.mujoco_simulation.mj_sim.data.get_site_xpos(
f"robot0:{site}"
)
for site in FINGERTIP_SITE_NAMES
]
)
return fingertip_pos.flatten()
class MujocoShadowHandJointPosObservation(MujocoShadowHandObservation):
"""
Mujoco based observation for shadowhand joint positions.
"""
def get(self) -> np.ndarray:
"""
Get shadowhand joint positions.
"""
return self.hand.observe().joint_positions()
class MujocoShadowHandJointVelocityObservation(MujocoShadowHandObservation):
"""
Mujoco based observation for shadowhand joint velocities.
"""
def get(self) -> np.ndarray:
"""
Get shadowhand joint velocities.
"""
return self.hand.observe().joint_velocities()
class MujocoShadowhandAngleObservation(MujocoShadowHandObservation):
"""
Mujoco based observation for shadowhand hand angle.
"""
def get(self) -> np.ndarray:
"""
Get shadowhand joint velocities.
"""
return self.provider.mujoco_simulation.get_qpos("hand_angle")
| 2,716 | 27.6 | 82 | py |
robogym | robogym-master/robogym/envs/dactyl/observation/full_perpendicular.py | import numpy as np
from robogym.observation.goal import GoalObservation
from robogym.observation.mujoco import MujocoObservation
from robogym.utils.rotation import normalize_angles
MYPY = False
if MYPY:
from robogym.envs.dactyl.full_perpendicular import FullPerpendicularSimulation
BaseObservationType = MujocoObservation[FullPerpendicularSimulation]
else:
BaseObservationType = MujocoObservation
class MujocoFaceAngleObservation(BaseObservationType):
"""
Implement mujoco base cube face angles.
"""
def get(self) -> np.ndarray:
"""
Get cube position.
"""
return normalize_angles(self.provider.mujoco_simulation.get_face_angles("cube"))
class GoalCubePosObservation(GoalObservation):
"""
Implement goal cube position observation.
"""
def get(self) -> np.ndarray:
"""
Get cube position.
"""
assert self.provider.goal
return self.provider.goal["cube_pos"]
class GoalFaceAngleObservation(GoalObservation):
"""
Implement goal cube face angle observation.
"""
def get(self) -> np.ndarray:
"""
Get cube position.
"""
assert self.provider.goal
return self.provider.goal["cube_face_angle"]
| 1,267 | 22.924528 | 88 | py |
robogym | robogym-master/robogym/envs/dactyl/observation/reach.py | import numpy as np
from robogym.observation.goal import GoalObservation
class GoalFingertipPosObservation(GoalObservation):
"""
Implement goal fingertip pos observation.
"""
def get(self) -> np.ndarray:
assert self.provider.goal
return self.provider.goal["fingertip_pos"]
class GoalIsAchievedObservation(GoalObservation):
"""
Implement observation indicating if we've achieved the current goal.
"""
def get(self) -> np.ndarray:
"""
Get the flag indicating if we've achieved the current goal.
"""
return self.provider.is_successful
| 618 | 22.807692 | 72 | py |
robogym | robogym-master/robogym/envs/dactyl/observation/face_perpendicular.py | import numpy as np
from robogym.observation.goal import GoalObservation
from robogym.observation.mujoco import MujocoObservation
MYPY = False
if MYPY:
from robogym.envs.dactyl.face_perpendicular import FacePerpendicularSimulation
BaseObservationType = MujocoObservation[FacePerpendicularSimulation]
else:
BaseObservationType = MujocoObservation
class MujocoFaceAngleObservation(BaseObservationType):
"""
Implement mujoco base cube face angles.
"""
def get(self) -> np.ndarray:
"""
Get cube position.
"""
return self.provider.mujoco_simulation.get_face_angles("cube")
class GoalCubePosObservation(GoalObservation):
"""
Implement goal cube position observation.
"""
def get(self) -> np.ndarray:
"""
Get cube position.
"""
assert self.provider.goal
return self.provider.goal["cube_pos"]
class GoalFaceAngleObservation(GoalObservation):
"""
Implement goal cube face angle observation.
"""
def get(self) -> np.ndarray:
"""
Get cube position.
"""
assert self.provider.goal
return self.provider.goal["cube_face_angle"]
| 1,197 | 22.038462 | 82 | py |
robogym | robogym-master/robogym/envs/dactyl/observation/locked.py | import numpy as np
from robogym.observation.goal import GoalObservation
class GoalCubePosObservation(GoalObservation):
"""
Implement goal cube position observation.
"""
def get(self) -> np.ndarray:
"""
Locked cube doesn't take position as part of goal.
"""
return np.zeros(3)
| 328 | 19.5625 | 58 | py |
robogym | robogym-master/robogym/envs/dactyl/tests/test_rubik_solvers.py | import unittest
import numpy as np
import pytest
from numpy.testing import assert_allclose
from robogym.envs.dactyl.full_perpendicular import make_env
from robogym.utils import rotation
class TestRubikSolvers(unittest.TestCase):
X_AXIS = 0
Y_AXIS = 1
Z_AXIS = 2
NEGATIVE_SIDE = 0
POSITIVE_SIDE = 1
CW = 1
CCW = -1
# Apply B R U rotations to solved cube
scrambles = [
{
"rotation": {
"axis": Z_AXIS,
"side": POSITIVE_SIDE,
"angle": CW * np.pi / 2,
},
"recovery_flips": np.array([[1, 1, 1]]),
},
{
"rotation": {
"axis": X_AXIS,
"side": POSITIVE_SIDE,
"angle": CW * np.pi / 2,
},
"recovery_flips": np.array([[0, 0, 1]]),
},
{
"rotation": {
"axis": Y_AXIS,
"side": POSITIVE_SIDE,
"angle": CW * np.pi / 2,
},
"recovery_flips": np.array([[0, 1, 1]]),
},
]
def test_face_cube_solver(self):
constants = {
"goal_generation": "face_cube_solver",
"num_scramble_steps": 3,
"randomize_face_angles": False,
"randomize": False,
}
env = make_env(constants=constants)
unwrapped = env.unwrapped
# start from deterministic straight qpos
unwrapped.mujoco_simulation.set_qpos("cube_rotation", [1.0, 0.0, 0.0, 0.0])
assert_allclose(
unwrapped.mujoco_simulation.get_qpos("cube_rotation"), [1.0, 0.0, 0.0, 0.0]
)
current_face_rotations = np.zeros(6)
for step in self.scrambles:
rot = step["rotation"]
unwrapped.mujoco_simulation.cube_model.rotate_face(
rot["axis"], rot["side"], rot["angle"]
)
current_face_rotations[rot["axis"] * 2 + rot["side"]] += rot["angle"]
# track remaining face rotations on cube
assert_allclose(
current_face_rotations, [0, np.pi / 2, 0, np.pi / 2, 0, np.pi / 2]
)
unwrapped.reset_goal_generation()
steps_left = len(self.scrambles)
for step in reversed(self.scrambles):
# assert state before recovery flip
_, reached, goal_info = env.unwrapped.goal_info()
assert not reached
assert goal_info["goal_reachable"]
assert goal_info["goal_dist"]["steps_to_solve"] == steps_left
goal = goal_info["goal"]
assert goal["goal_type"] == "flip"
# check if expected quat goal is met
recovery_quat = rotation.apply_euler_rotations(
unwrapped.mujoco_simulation.get_qpos("cube_rotation"),
step["recovery_flips"],
)
assert_allclose(goal["cube_quat"], recovery_quat, atol=1e-8)
assert_allclose(goal["cube_face_angle"], current_face_rotations)
# apply target quat rotation to cube and recompute goal
unwrapped.mujoco_simulation.set_qpos("cube_rotation", recovery_quat)
unwrapped.update_goal_info()
_, reached, info = unwrapped.goal_info()
assert reached
unwrapped.mujoco_simulation.forward()
unwrapped.reset_goal()
solution = step["rotation"]
_, reached, goal_info = env.unwrapped.goal_info()
assert not reached
assert goal_info["goal_reachable"]
assert goal_info["goal_dist"]["steps_to_solve"] == steps_left
goal = goal_info["goal"]
assert goal["goal_type"] == "rotation"
assert goal["axis_nr"] == solution["axis"]
assert goal["axis_sign"][0] == solution["side"]
current_face_rotations[solution["axis"] * 2 + solution["side"]] -= solution[
"angle"
]
assert_allclose(goal["cube_face_angle"], current_face_rotations)
# actually rotate cube in the opposite direction of the original rotation
unwrapped.mujoco_simulation.cube_model.rotate_face(
solution["axis"], solution["side"], -solution["angle"]
)
unwrapped.update_goal_info()
_, reached, info = unwrapped.goal_info()
assert reached
unwrapped.mujoco_simulation.forward()
unwrapped.reset_goal()
steps_left -= 1
assert steps_left == 0
def test_release_cube_solver(self):
constants = {
"goal_generation": "release_cube_solver",
"num_scramble_steps": 3,
"randomize_face_angles": False,
"randomize": False,
}
env = make_env(constants=constants)
unwrapped = env.unwrapped
# start from deterministic straight qpos
unwrapped.mujoco_simulation.set_qpos("cube_rotation", [1.0, 0.0, 0.0, 0.0])
assert_allclose(
unwrapped.mujoco_simulation.get_qpos("cube_rotation"), [1.0, 0.0, 0.0, 0.0]
)
current_face_rotations = np.zeros(6)
for step in self.scrambles:
rot = step["rotation"]
unwrapped.mujoco_simulation.cube_model.rotate_face(
rot["axis"], rot["side"], rot["angle"]
)
current_face_rotations[rot["axis"] * 2 + rot["side"]] += rot["angle"]
# track remaining face rotations on cube
assert_allclose(
current_face_rotations, [0, np.pi / 2, 0, np.pi / 2, 0, np.pi / 2]
)
unwrapped.reset_goal_generation()
steps_left = len(self.scrambles)
for step in reversed(self.scrambles):
# assert state before recovery flip
_, reached, goal_info = env.unwrapped.goal_info()
assert not reached
assert goal_info["goal_reachable"]
assert goal_info["goal_dist"]["steps_to_solve"] == steps_left
goal = goal_info["goal"]
assert goal["goal_type"] == "flip"
# check if expected quat goal is met
recovery_quat = rotation.apply_euler_rotations(
unwrapped.mujoco_simulation.get_qpos("cube_rotation"),
step["recovery_flips"],
)
assert_allclose(goal["cube_quat"], recovery_quat, atol=1e-8)
assert_allclose(goal["cube_face_angle"], current_face_rotations)
# apply target quat rotation to cube and recompute goal
unwrapped.mujoco_simulation.set_qpos("cube_rotation", recovery_quat)
unwrapped.update_goal_info()
_, reached, info = unwrapped.goal_info()
assert reached
unwrapped.mujoco_simulation.forward()
unwrapped.reset_goal()
solution = step["rotation"]
_, reached, goal_info = env.unwrapped.goal_info()
assert not reached
assert goal_info["goal_reachable"]
assert goal_info["goal_dist"]["steps_to_solve"] == steps_left
goal = goal_info["goal"]
assert goal["goal_type"] == "rotation"
assert goal["axis_nr"] == solution["axis"]
assert goal["axis_sign"][0] == solution["side"]
current_face_rotations[solution["axis"] * 2 + solution["side"]] -= solution[
"angle"
]
assert_allclose(goal["cube_face_angle"], current_face_rotations)
# actually rotate cube in the opposite direction of the original rotation
unwrapped.mujoco_simulation.cube_model.rotate_face(
solution["axis"], solution["side"], -solution["angle"]
)
unwrapped.update_goal_info()
_, reached, info = unwrapped.goal_info()
assert reached
unwrapped.mujoco_simulation.forward()
unwrapped.reset_goal()
steps_left -= 1
assert steps_left == 0
_, _, info = unwrapped.goal_info()
assert info["solved"]
unwrapped.mujoco_simulation.forward()
assert info["solved"]
def test_unconstrained_cube_solver(self):
constants = {
"goal_generation": "unconstrained_cube_solver",
"num_scramble_steps": 3,
"randomize_face_angles": False,
"randomize": False,
}
env = make_env(constants=constants)
unwrapped = env.unwrapped
# start from deterministic straight qpos
unwrapped.mujoco_simulation.set_qpos("cube_rotation", [1.0, 0.0, 0.0, 0.0])
assert_allclose(
unwrapped.mujoco_simulation.get_qpos("cube_rotation"), [1.0, 0.0, 0.0, 0.0]
)
current_face_rotations = np.zeros(6)
for step in self.scrambles:
rot = step["rotation"]
unwrapped.mujoco_simulation.cube_model.rotate_face(
rot["axis"], rot["side"], rot["angle"]
)
current_face_rotations[rot["axis"] * 2 + rot["side"]] += rot["angle"]
# track remaining face rotations on cube
assert_allclose(
current_face_rotations, [0, np.pi / 2, 0, np.pi / 2, 0, np.pi / 2]
)
unwrapped.reset_goal_generation()
steps_left = len(self.scrambles)
for step in reversed(self.scrambles):
solution = step["rotation"]
_, reached, goal_info = env.unwrapped.goal_info()
assert not reached
assert goal_info["goal_reachable"]
assert goal_info["goal_dist"]["steps_to_solve"] == steps_left
goal = goal_info["goal"]
assert goal["goal_type"] == "rotation"
current_face_rotations[solution["axis"] * 2 + solution["side"]] -= solution[
"angle"
]
assert_allclose(goal["cube_face_angle"], current_face_rotations)
# actually rotate cube in the opposite direction of the original rotation
unwrapped.mujoco_simulation.cube_model.rotate_face(
solution["axis"], solution["side"], -solution["angle"]
)
unwrapped.update_goal_info()
_, reached, info = unwrapped.goal_info()
assert reached
unwrapped.mujoco_simulation.forward()
unwrapped.reset_goal()
steps_left -= 1
assert steps_left == 0
@pytest.mark.parametrize("axis", [0, 1, 2])
@pytest.mark.parametrize("side", [0, 1])
@pytest.mark.parametrize("rot_direction", [-1, 1])
def test_unconstrained_cube_solver(axis, side, rot_direction):
constants = {
"goal_generation": "unconstrained_cube_solver",
"num_scramble_steps": 0,
"randomize_face_angles": False,
"randomize": False,
}
env = make_env(constants=constants)
unwrapped = env.unwrapped
# Rotate each face and make sure goal generator is able to solve the cube in one step
unwrapped.mujoco_simulation.cube_model.rotate_face(
axis, side, np.pi / 2 * rot_direction
)
unwrapped.reset_goal_generation()
_, _, goal_info = env.unwrapped.goal_info()
assert goal_info["goal_reachable"]
assert goal_info["goal_dist"]["steps_to_solve"] == 1
goal = goal_info["goal"]
assert goal["goal_type"] == "rotation"
assert_allclose(goal["cube_face_angle"], np.zeros(6))
| 11,388 | 34.369565 | 89 | py |
robogym | robogym-master/robogym/envs/dactyl/tests/test_locked.py | import numpy as np
from mujoco_py import ignore_mujoco_warnings
from numpy.testing import assert_allclose
from robogym.envs.dactyl.common.cube_utils import on_palm
from robogym.envs.dactyl.locked import make_env, make_simple_env
from robogym.utils import rotation
def test_locked_cube():
env = make_env(starting_seed=0)
is_on_palm = []
for idx in range(20):
env.reset()
expected_joints = (
"cube:cube_tx",
"cube:cube_ty",
"cube:cube_tz",
"cube:cube_rot",
"target:cube_tx",
"target:cube_ty",
"target:cube_tz",
"target:cube_rot",
"robot0:WRJ1",
"robot0:WRJ0",
"robot0:FFJ3",
"robot0:FFJ2",
"robot0:FFJ1",
"robot0:FFJ0",
"robot0:MFJ3",
"robot0:MFJ2",
"robot0:MFJ1",
"robot0:MFJ0",
"robot0:RFJ3",
"robot0:RFJ2",
"robot0:RFJ1",
"robot0:RFJ0",
"robot0:LFJ4",
"robot0:LFJ3",
"robot0:LFJ2",
"robot0:LFJ1",
"robot0:LFJ0",
"robot0:THJ4",
"robot0:THJ3",
"robot0:THJ2",
"robot0:THJ1",
"robot0:THJ0",
)
assert env.unwrapped.sim.model.joint_names == expected_joints
with ignore_mujoco_warnings():
for _ in range(20):
obs, _, _, _ = env.step(env.action_space.nvec // 2)
is_on_palm.append(on_palm(env.unwrapped.sim))
# Make sure the mass is right.
cube_body_idx = env.unwrapped.sim.model.body_name2id("cube:middle")
assert_allclose(
env.unwrapped.sim.model.body_subtreemass[cube_body_idx], 0.078, atol=1e-3
)
assert (
np.mean(is_on_palm) >= 0.8
), "Cube should stay in hand (most of the time) when zero action is sent."
def test_observe():
# Test observation matches simulation state.
env = make_simple_env()
env.reset()
simulation = env.mujoco_simulation
obs = env.observe()
qpos = simulation.qpos
qpos[simulation.qpos_idxs["target_all_joints"]] = 0.0
qvel = simulation.qvel
qvel[simulation.qvel_idxs["target_all_joints"]] = 0.0
true_obs = {
"cube_pos": simulation.get_qpos("cube_position"),
"cube_quat": rotation.quat_normalize(simulation.get_qpos("cube_rotation")),
"hand_angle": simulation.get_qpos("hand_angle"),
"fingertip_pos": simulation.shadow_hand.observe()
.fingertip_positions()
.flatten(),
"qpos": qpos,
"qvel": qvel,
}
for obs_key, true_val in true_obs.items():
assert np.allclose(
obs[obs_key], true_val
), f"Value for obs {obs_key} {obs[obs_key]} doesn't match true value {true_val}."
def test_informative_obs():
WHITELIST = [
# The position of the goal is zeroed
"relative_goal_pos",
"noisy_relative_goal_pos",
"goal_pos",
# Not all episodes end with a fall, i.e. it might be all zeros
"fell_down",
"is_goal_achieved",
]
env = make_env(constants=dict(randomize=False, max_timesteps_per_goal=50))
obs = env.reset()
done = False
all_obs = [obs]
while not done:
obs, reward, done, info = env.step(env.action_space.sample())
all_obs.append(obs)
all_obs.append(env.reset()) # one more reset at the end
# Collect all obs and index by key.
keys = set(all_obs[0].keys())
assert len(keys) > 0
combined_obs_by_keys = {key: [] for key in keys}
for obs in all_obs:
assert set(obs.keys()) == keys
for key in keys:
combined_obs_by_keys[key].append(obs[key])
# Make sure that none of the keys has all-constant obs.
for key, obs in combined_obs_by_keys.items():
assert len(obs) == len(all_obs)
if key in WHITELIST:
continue
obs0 = obs[0]
equals = [np.array_equal(obs0, obs_i) for obs_i in obs]
# If ob0 is equal to all other obs, all obs are equal, i.e. the observation
# contains no information whatsoever. This is usually bad (e.g. we had an issue
# in the past where qpos was aways set to all-zeros).
assert not np.all(equals), "observations for {} are all equal to {}".format(
key, obs0
)
def test_relative_action():
for relative_action in [True, False]:
# NOTE - this seed choice is very important, since starting states affect the test.
# The test could be updated to be robust in the future.
env = make_env(
starting_seed=586895, constants={"relative_action": relative_action}
)
env.reset()
zeros = np.zeros(env.unwrapped.sim.model.nu)
not_zeros = np.ones(env.unwrapped.sim.model.nu) * 0.5
num_robot_joints = len(
[x for x in env.unwrapped.sim.model.joint_names if "robot0" in x]
)
qpos_shape = env.unwrapped.sim.data.qpos.shape
num_cube_joints = qpos_shape[0] - num_robot_joints
for action in [zeros, not_zeros]:
env.unwrapped.sim.data.qvel[:] = 0
env.unwrapped.sim.data.qpos[:] = np.random.randn(*qpos_shape) * 0.1
env.unwrapped.sim.data.qpos[:num_cube_joints] = -10.0
for _ in range(10):
env.unwrapped.step(action)
qvel = np.sum(np.square(env.unwrapped.sim.data.qvel[num_cube_joints:]))
if (action == zeros).all() and relative_action:
assert qvel < 0.09
else:
assert qvel > 0.09
def helper_test_two_deterministic_envs(env1, env2):
env1.reset()
env2.reset()
env1.unwrapped.reset_goal()
env2.unwrapped.reset_goal()
action = env2.action_space.sample()
env1_obs, env1_reward = env1.step(action)[:2]
env2_obs, env2_reward = env2.step(action)[:2]
for key in env1_obs.keys():
assert np.all(
np.isclose(env1_obs[key], env2_obs[key])
), "Key: %s -- Diff:\n%s" % (key, env1_obs[key] - env2_obs[key])
assert np.allclose(env1_reward, env2_reward)
def test_rand_locked_consistent():
seed = 12345
helper_test_two_deterministic_envs(
make_env(starting_seed=seed), make_env(starting_seed=seed)
)
def test_det_locked_consistent():
seed = 12345
helper_test_two_deterministic_envs(
make_env(constants=dict(randomize=False), starting_seed=seed),
make_env(constants=dict(randomize=False), starting_seed=seed),
)
| 6,618 | 30.975845 | 91 | py |
robogym | robogym-master/robogym/envs/dactyl/tests/test_full.py | import numpy as np
import pytest
from numpy.testing import assert_allclose
from robogym.envs.dactyl.full_perpendicular import make_env, make_simple_env
from robogym.utils import rotation
def test_cube_mass():
env = make_env(constants=dict(randomize=False))
sim = env.unwrapped.sim
cube_id = sim.model.body_name2id("cube:middle")
# The mass of the giiker cube is 90g
assert_allclose(sim.model.body_subtreemass[cube_id], 0.09, atol=0.005)
@pytest.mark.parametrize(
"goal_generation",
[
"face_curr",
"face_free",
"face_cube_solver",
"unconstrained_cube_solver",
"full_unconstrained",
"release_cube_solver",
],
)
def test_goal_info(goal_generation):
constants = {
"goal_generation": goal_generation,
"randomize_face_angles": False,
"randomize": False,
}
# There is some small chance that cube can get into invalid state in simulation
# which will cause cube solver to fail. Fixing the seed here to mitigate this
# issue.
env = make_env(constants=constants, starting_seed=12312)
env.reset()
_, _, goal_info = env.unwrapped.goal_info()
assert "goal" in goal_info
assert "goal_type" in goal_info["goal"]
def test_make_simple_env():
env = make_simple_env(
parameters={
"simulation_params": dict(cube_appearance="vision", hide_target=True)
}
)
env.reset()
sim = env.sim # there is no wrapper.
sticker_geoms = [g for g in sim.model.geom_names if g.startswith("cube:sticker:")]
assert len(sticker_geoms) == 9 * 6
def test_observe():
# Test observation matches simulation state.
env = make_simple_env()
env.reset()
simulation = env.mujoco_simulation
obs = env.observe()
qpos = simulation.qpos
qpos[simulation.qpos_idxs["target_all_joints"]] = 0.0
qvel = simulation.qvel
qvel[simulation.qvel_idxs["target_all_joints"]] = 0.0
true_obs = {
"cube_pos": simulation.get_qpos("cube_position"),
"cube_quat": rotation.quat_normalize(simulation.get_qpos("cube_rotation")),
"hand_angle": simulation.get_qpos("hand_angle"),
"fingertip_pos": simulation.shadow_hand.observe()
.fingertip_positions()
.flatten(),
"qpos": qpos,
"qvel": qvel,
}
for obs_key, true_val in true_obs.items():
assert np.allclose(
obs[obs_key], true_val
), f"Value for obs {obs_key} {obs[obs_key]} doesn't match true value {true_val}."
def test_informative_obs():
WHITELIST = [
# The position of the goal is zeroed
"relative_goal_pos",
"noisy_relative_goal_pos",
"goal_pos",
# Not all episodes end with a fall, i.e. it might be all zeros
"fell_down",
]
env = make_env(constants=dict(randomize=False, max_timesteps_per_goal=50))
obs = env.reset()
done = False
all_obs = [obs]
while not done:
obs, reward, done, info = env.step(env.action_space.sample())
all_obs.append(obs)
all_obs.append(env.reset()) # one more reset at the end
# Collect all obs and index by key.
keys = set(all_obs[0].keys())
assert len(keys) > 0
combined_obs_by_keys = {key: [] for key in keys}
for obs in all_obs:
assert set(obs.keys()) == keys
for key in keys:
combined_obs_by_keys[key].append(obs[key])
# Make sure that none of the keys has all-constant obs.
for key, obs in combined_obs_by_keys.items():
assert len(obs) == len(all_obs)
if key in WHITELIST:
continue
obs0 = obs[0]
equals = [np.array_equal(obs0, obs_i) for obs_i in obs]
# If ob0 is equal to all other obs, all obs are equal, i.e. the observation
# contains no information whatsoever. This is usually bad (e.g. we had an issue
# in the past where qpos was aways set to all-zeros).
assert not np.all(equals), "observations for {} are all equal to {}".format(
key, obs0
)
def test_min_episode_length():
min_steps = 1000
env_1 = make_env(constants=dict(min_episode_length=min_steps), starting_seed=12312)
env_1.reset()
# fix seed to avoid stochastic tests
num_fallen = 0
for _ in range(min_steps):
o, r, d, i = env_1.step(env_1.action_space.sample())
assert not d
if i["fell_down"]:
num_fallen += 1
assert num_fallen > 0
env_2 = make_env(constants=dict(min_episode_length=-1), starting_seed=12312)
env_2.reset()
# fix seed to avoid stochastic tests
for t in range(min_steps):
o, r, d, i = env_2.step(env_2.action_space.sample())
if d:
break
assert t < min_steps - 1
| 4,773 | 29.8 | 89 | py |
robogym | robogym-master/robogym/envs/dactyl/tests/test_cube_utils.py | import numpy as np
import robogym.envs.dactyl.common.cube_utils as cube_utils
import robogym.utils.rotation as rotation
def test_align_quat_up():
""" Test function 'align_quat_up' """
identity_quat = np.array([1.0, 0.0, 0.0, 0.0])
assert (
np.linalg.norm(cube_utils.align_quat_up(identity_quat) - identity_quat) < 1e-8
)
# Rotate along each axis but only slightly
transformations = np.eye(3) * 0.4
for i in range(3):
quat = rotation.euler2quat(transformations[i])
# For axes 0, 1 identity rotation is the proper rotation
if i in [0, 1]:
assert np.linalg.norm(cube_utils.align_quat_up(quat) - identity_quat) < 1e-8
else:
# For axis 2 cube is already aligned
assert np.linalg.norm(cube_utils.align_quat_up(quat) - quat) < 1e-8
# Rotate along each axis so much that another face is now on top
transformations = np.eye(3) * (np.pi / 2 - 0.3)
full_transformations = np.eye(3) * (np.pi / 2)
for i in range(3):
quat = rotation.euler2quat(transformations[i])
aligned = cube_utils.align_quat_up(quat)
if i in [0, 1]:
new_euler_angles = rotation.quat2euler(aligned)
assert np.linalg.norm(new_euler_angles - full_transformations[i]) < 1e-8
else:
# For axis 2 cube is already aligned
assert np.linalg.norm(cube_utils.align_quat_up(quat) - quat) < 1e-8
def test_up_axis_with_sign():
""" Test function 'up_axis_with_sign' """
identity_quat = np.array([1.0, 0.0, 0.0, 0.0])
assert cube_utils.up_axis_with_sign(identity_quat) == (2, 1)
# Rotate along each axis so much that another face is now on top
transformations = np.eye(3) * (np.pi / 2 - 0.3)
for i in range(3):
quat = rotation.euler2quat(transformations[i])
axis, sign = cube_utils.up_axis_with_sign(quat)
if i == 0:
assert axis == 1
assert sign == 1
elif i == 1:
assert axis == 0
assert sign == -1
else:
assert axis == 2
assert sign == 1
transformations = -np.eye(3) * (np.pi / 2 - 0.3)
for i in range(3):
quat = rotation.euler2quat(transformations[i])
axis, sign = cube_utils.up_axis_with_sign(quat)
if i == 0:
assert axis == 1
assert sign == -1
elif i == 1:
assert axis == 0
assert sign == 1
else:
assert axis == 2
assert sign == 1
def test_distance_quat_from_being_up():
""" Test function 'distance_quat_from_being_up' """
initial_configuration = np.array([1.0, 0.0, 0.0, 0.0])
assert (
np.linalg.norm(
cube_utils.distance_quat_from_being_up(initial_configuration, 2, 1)
- initial_configuration
)
< 1e-8
)
assert (
np.abs(
rotation.quat_magnitude(
cube_utils.distance_quat_from_being_up(initial_configuration, 2, -1)
)
- np.pi
)
< 1e-8
)
assert (
np.abs(
rotation.quat_magnitude(
cube_utils.distance_quat_from_being_up(initial_configuration, 0, 1)
)
- np.pi / 2
)
< 1e-8
)
assert (
np.abs(
rotation.quat_magnitude(
cube_utils.distance_quat_from_being_up(initial_configuration, 0, -1)
)
- np.pi / 2
)
< 1e-8
)
assert (
np.abs(
rotation.quat_magnitude(
cube_utils.distance_quat_from_being_up(initial_configuration, 1, 1)
)
- np.pi / 2
)
< 1e-8
)
assert (
np.abs(
rotation.quat_magnitude(
cube_utils.distance_quat_from_being_up(initial_configuration, 1, -1)
)
- np.pi / 2
)
< 1e-8
)
# Rotate along each axis but only slightly
transformations = np.eye(3) * 0.4
for i in range(3):
quat = rotation.euler2quat(transformations[i])
distance_quat = cube_utils.distance_quat_from_being_up(quat, 2, 1)
if i in [0, 1]:
result = rotation.quat_mul(quat, distance_quat)
assert np.linalg.norm(result - initial_configuration) < 1e-8
else:
assert np.linalg.norm(distance_quat - initial_configuration) < 1e-8
transformations = np.eye(3) * (np.pi / 2 - 0.3)
for i in range(3):
quat = rotation.euler2quat(transformations[i])
if i == 0:
distance_quat = cube_utils.distance_quat_from_being_up(quat, 1, 1)
assert np.abs(rotation.quat_magnitude(distance_quat) - 0.3) < 1e-8
elif i == 1:
distance_quat = cube_utils.distance_quat_from_being_up(quat, 0, -1)
assert np.abs(rotation.quat_magnitude(distance_quat) - 0.3) < 1e-8
else:
distance_quat = cube_utils.distance_quat_from_being_up(quat, 2, 1)
assert np.linalg.norm(distance_quat - initial_configuration) < 1e-8
| 5,150 | 28.267045 | 88 | py |
robogym | robogym-master/robogym/envs/dactyl/tests/test_cube_manipulator.py | import numpy as np
import pycuber
import robogym.utils.rotation as rotation
from robogym.envs.dactyl.full_perpendicular import FullPerpendicularSimulation
X_AXIS = 0
Y_AXIS = 1
Z_AXIS = 2
NEGATIVE_SIDE = 0
POSITIVE_SIDE = 1
def _full_side_idx(axis, side):
# DRIVER ORDER IS:
# -x, +x, -y, +y, -z, +z
return axis * 2 + side
def test_cube_manipulator_drivers():
"""
Test CubeManipulator class if it manages to manipulates cubelets properly
"""
mujoco_simulation = FullPerpendicularSimulation.build(n_substeps=10)
for axis in [X_AXIS, Y_AXIS, Z_AXIS]:
for side in [NEGATIVE_SIDE, POSITIVE_SIDE]:
# Reset simulation
mujoco_simulation.set_qpos("cube_all_joints", 0.0)
mujoco_simulation.cube_model.rotate_face(axis, side, np.pi / 2)
target_angle = np.zeros(6, dtype=float)
target_angle[_full_side_idx(axis, side)] = np.pi / 2
assert (
np.linalg.norm(
mujoco_simulation.get_qpos("cube_drivers") - target_angle
)
< 1e-6
)
def test_cube_manipulator_drivers_sequence():
"""
Test CubeManipulator class if it manages to manipulate cubelets properly
"""
mujoco_simulation = FullPerpendicularSimulation.build(n_substeps=10)
mujoco_simulation.cube_model.rotate_face(X_AXIS, POSITIVE_SIDE, np.pi / 2)
mujoco_simulation.cube_model.rotate_face(Y_AXIS, POSITIVE_SIDE, np.pi / 2)
mujoco_simulation.cube_model.rotate_face(Z_AXIS, POSITIVE_SIDE, np.pi / 2)
mujoco_simulation.cube_model.rotate_face(Y_AXIS, POSITIVE_SIDE, np.pi / 2)
mujoco_simulation.cube_model.rotate_face(X_AXIS, POSITIVE_SIDE, np.pi / 2)
target_angle = np.array([0.0, np.pi, 0.0, np.pi, 0.0, np.pi / 2])
assert (
np.linalg.norm(mujoco_simulation.get_qpos("cube_drivers") - target_angle) < 1e-6
)
POSSIBLE_COORDS = [-1, 0, 1]
def _assert_cubelet_coords(manipulator, original_coords, current_coords):
""" Check if given cubelet is present at given coords"""
indexes = original_coords.round().astype(int) + 1
coord_idx = indexes[0] * 9 + indexes[1] * 3 + indexes[2]
meta_info = manipulator.cubelet_meta_info[coord_idx]
assert np.linalg.norm(meta_info["coords"] - original_coords) < 1e-6
if meta_info["type"] == "cubelet":
mtx = manipulator._cubelet_rotation_matrix(meta_info, manipulator.sim.data.qpos)
actual_current_coords = mtx @ original_coords.astype(float)
assert np.linalg.norm(actual_current_coords - current_coords) < 1e-6
def test_cube_manipulator_cubelet_positions():
"""
Test CubeManipulator class if it manages to manipulates cubelets properly
"""
mujoco_simulation = FullPerpendicularSimulation.build(n_substeps=10)
for x_coord in POSSIBLE_COORDS:
for y_coord in POSSIBLE_COORDS:
for z_coord in POSSIBLE_COORDS:
coords = np.array([x_coord, y_coord, z_coord])
_assert_cubelet_coords(mujoco_simulation.cube_model, coords, coords)
mujoco_simulation.cube_model.rotate_face(X_AXIS, POSITIVE_SIDE, np.pi / 2)
# These are not touched
for x_coord in [0, -1]:
for y_coord in POSSIBLE_COORDS:
for z_coord in POSSIBLE_COORDS:
coords = np.array([x_coord, y_coord, z_coord])
_assert_cubelet_coords(mujoco_simulation.cube_model, coords, coords)
# Let's check four corner cubelets just to be sure
_assert_cubelet_coords(
mujoco_simulation.cube_model, np.array([1, 1, 1]), np.array([1, -1, 1])
)
_assert_cubelet_coords(
mujoco_simulation.cube_model, np.array([1, -1, 1]), np.array([1, -1, -1])
)
_assert_cubelet_coords(
mujoco_simulation.cube_model, np.array([1, -1, -1]), np.array([1, 1, -1])
)
_assert_cubelet_coords(
mujoco_simulation.cube_model, np.array([1, 1, -1]), np.array([1, 1, 1])
)
mujoco_simulation.cube_model.rotate_face(Y_AXIS, POSITIVE_SIDE, np.pi / 2)
_assert_cubelet_coords(
mujoco_simulation.cube_model, np.array([-1, 1, -1]), np.array([-1, 1, 1])
)
_assert_cubelet_coords(
mujoco_simulation.cube_model, np.array([-1, 1, 1]), np.array([1, 1, 1])
)
_assert_cubelet_coords(
mujoco_simulation.cube_model, np.array([1, 1, -1]), np.array([1, 1, -1])
)
_assert_cubelet_coords(
mujoco_simulation.cube_model, np.array([1, -1, -1]), np.array([-1, 1, -1])
)
def test_snap_rotate_face_with_threshold():
from robogym.envs.dactyl.full_perpendicular import FullPerpendicularSimulation
mujoco_simulation = FullPerpendicularSimulation.build(n_substeps=10)
mujoco_simulation.cube_model.snap_rotate_face_with_threshold(
X_AXIS, POSITIVE_SIDE, np.pi / 2
)
# These are not touched
for x_coord in [0, -1]:
for y_coord in POSSIBLE_COORDS:
for z_coord in POSSIBLE_COORDS:
coords = np.array([x_coord, y_coord, z_coord])
_assert_cubelet_coords(mujoco_simulation.cube_model, coords, coords)
# Let's check four corner cubelets just to be sure
_assert_cubelet_coords(
mujoco_simulation.cube_model, np.array([1, 1, 1]), np.array([1, -1, 1])
)
_assert_cubelet_coords(
mujoco_simulation.cube_model, np.array([1, -1, 1]), np.array([1, -1, -1])
)
_assert_cubelet_coords(
mujoco_simulation.cube_model, np.array([1, -1, -1]), np.array([1, 1, -1])
)
_assert_cubelet_coords(
mujoco_simulation.cube_model, np.array([1, 1, -1]), np.array([1, 1, 1])
)
# Rotate this face again by 45 degrees
mujoco_simulation.cube_model.snap_rotate_face_with_threshold(
X_AXIS, POSITIVE_SIDE, np.pi / 4
)
cubelets_before = mujoco_simulation.get_qpos("cube_cubelets").copy()
# None of these should do anything
mujoco_simulation.cube_model.snap_rotate_face_with_threshold(
Y_AXIS, POSITIVE_SIDE, np.pi / 8
)
mujoco_simulation.cube_model.snap_rotate_face_with_threshold(
Y_AXIS, NEGATIVE_SIDE, np.pi / 8
)
mujoco_simulation.cube_model.snap_rotate_face_with_threshold(
Z_AXIS, POSITIVE_SIDE, np.pi / 8
)
mujoco_simulation.cube_model.snap_rotate_face_with_threshold(
Z_AXIS, NEGATIVE_SIDE, np.pi / 8
)
cubelets_after = mujoco_simulation.get_qpos("cube_cubelets").copy()
assert np.linalg.norm(cubelets_before - cubelets_after) < 1e-6
# Revert
mujoco_simulation.cube_model.snap_rotate_face_with_threshold(
X_AXIS, POSITIVE_SIDE, -np.pi / 4
)
# Move a little
mujoco_simulation.cube_model.snap_rotate_face_with_threshold(
X_AXIS, POSITIVE_SIDE, 0.05
)
# Make sure cube gets realigned
mujoco_simulation.cube_model.snap_rotate_face_with_threshold(
Y_AXIS, POSITIVE_SIDE, np.pi / 2
)
_assert_cubelet_coords(
mujoco_simulation.cube_model, np.array([-1, 1, -1]), np.array([-1, 1, 1])
)
_assert_cubelet_coords(
mujoco_simulation.cube_model, np.array([-1, 1, 1]), np.array([1, 1, 1])
)
_assert_cubelet_coords(
mujoco_simulation.cube_model, np.array([1, 1, -1]), np.array([1, 1, -1])
)
_assert_cubelet_coords(
mujoco_simulation.cube_model, np.array([1, -1, -1]), np.array([-1, 1, -1])
)
cubelets_final = rotation.normalize_angles(
mujoco_simulation.get_qpos("cube_cubelets").copy()
)
assert (
np.linalg.norm(
cubelets_final - rotation.round_to_straight_angles(cubelets_final)
)
< 1e-8
)
def test_pycuber_conversion():
from robogym.envs.dactyl.full_perpendicular import FullPerpendicularSimulation
mujoco_simulation = FullPerpendicularSimulation.build()
for i in range(5):
cube = pycuber.Cube()
for action in np.random.choice(list("LRFBDU"), size=20, replace=True):
cube(str(action))
mujoco_simulation.cube_model.from_pycuber(cube)
cube2 = mujoco_simulation.cube_model.to_pycuber()
assert cube == cube2
| 8,123 | 30.984252 | 88 | py |
robogym | robogym-master/robogym/envs/dactyl/tests/test_reach.py | from robogym.envs.dactyl.reach import make_env
def test_dactyl_reach():
env = make_env()
obs = env.reset()
expected_joints = (
"robot0:WRJ1",
"robot0:WRJ0",
"robot0:FFJ3",
"robot0:FFJ2",
"robot0:FFJ1",
"robot0:FFJ0",
"robot0:MFJ3",
"robot0:MFJ2",
"robot0:MFJ1",
"robot0:MFJ0",
"robot0:RFJ3",
"robot0:RFJ2",
"robot0:RFJ1",
"robot0:RFJ0",
"robot0:LFJ4",
"robot0:LFJ3",
"robot0:LFJ2",
"robot0:LFJ1",
"robot0:LFJ0",
"robot0:THJ4",
"robot0:THJ3",
"robot0:THJ2",
"robot0:THJ1",
"robot0:THJ0",
)
assert env.unwrapped.sim.model.joint_names == expected_joints
for k, ob in obs.items():
assert ob.shape[0] > 0
| 826 | 21.972222 | 65 | py |
robogym | robogym-master/robogym/envs/dactyl/goals/full_unconstrained.py | import typing
import numpy as np
from robogym.envs.dactyl.common import cube_utils
from robogym.goal.goal_generator import GoalGenerator
from robogym.utils import rotation
class FullUnconstrainedGoal(GoalGenerator):
"""
Rotate any face, no orientation objectives for the Z axis.
"""
def __init__(
self,
mujoco_simulation,
success_threshold: dict,
face_geom_names: typing.List[str],
goal_directions: typing.Optional[typing.List[str]] = None,
round_target_face: bool = True,
):
"""
Create new FullUnconstrainedGoal object
:param mujoco_simulation: A SimulationInterface object for a mujoco simulation considered
:param success_threshold: Dictionary of threshold levels for cube orientation and face
rotation, for which we consider the cube "aligned" with the goal
:param face_geom_names: Names of 6 face geoms of the cube for which we measure the rotation
:param goal_directions: Whether to rotate faces only clockwise, counterclockwise or both
:param round_target_face: Whether target face rotations should be only round angles
(multiplies of pi/2) or not
:param p_face_flip: If the cube is aligned, what is the probability of flipping the cube
vs rotating the face
"""
super().__init__()
assert len(face_geom_names) == 6, "Only supports full cube for now"
self.mujoco_simulation = mujoco_simulation
self.success_threshold = success_threshold
self.face_geom_names = face_geom_names
if goal_directions is None:
self.goal_directions = ["cw", "ccw"]
else:
self.goal_directions = goal_directions
self.round_target_face = round_target_face
self.goal_candidates = list(range(len(self.face_geom_names)))
def next_goal(self, random_state, current_state):
""" Generate a new goal from current cube goal state """
cube_face = current_state["cube_face_angle"]
self.mujoco_simulation.clone_target_from_cube()
self.mujoco_simulation.target_model.soft_align_faces()
# Make the goal so that any face is rotated at random
face_to_shift = random_state.choice(self.goal_candidates)
# Rotate given face by a random angle and return both, new rotations and an angle
goal_face, delta_angle = cube_utils.rotated_face_with_angle(
cube_face,
face_to_shift,
random_state,
self.round_target_face,
directions=self.goal_directions,
)
self.mujoco_simulation.target_model.rotate_face(
face_to_shift // 2, face_to_shift % 2, delta_angle
)
return {
"cube_pos": np.zeros(3),
"cube_quat": np.zeros(4),
"cube_face_angle": goal_face,
"goal_type": "rotation",
}
def current_state(self):
""" Extract current cube goal state """
return {
"cube_pos": self.mujoco_simulation.get_qpos("cube_position"),
"cube_quat": self.mujoco_simulation.get_qpos("cube_rotation"),
"cube_face_angle": self.mujoco_simulation.get_face_angles("cube"),
}
def relative_goal(self, goal_state, current_state):
"""
Calculate a difference in the 'goal space' between current state and the target goal
"""
assert goal_state["goal_type"] == "rotation"
return {
# Cube pos does not count
"cube_pos": np.zeros(goal_state["cube_pos"].shape),
# Quaternion difference of a rotation
"cube_quat": np.zeros(goal_state["cube_quat"].shape),
# Angle differences
"cube_face_angle": rotation.normalize_angles(
goal_state["cube_face_angle"] - current_state["cube_face_angle"]
),
}
def goal_distance(self, goal_state, current_state):
""" Distance from the current goal to the target state. """
relative_goal = self.relative_goal(goal_state, current_state)
goal_distance = {
"cube_pos": 0.0,
"cube_quat": 0.0,
"cube_face_angle": np.linalg.norm(relative_goal["cube_face_angle"]),
}
return goal_distance
def goal_types(self) -> typing.Set[str]:
return {"rotation"}
| 4,409 | 35.147541 | 99 | py |
robogym | robogym-master/robogym/envs/dactyl/goals/unconstrained_cube_solver.py | import logging
import typing
import numpy as np
from robogym.envs.dactyl.goals.rubik_cube_solver import RubikCubeSolver
from robogym.utils import rotation
logger = logging.getLogger(__name__)
class UnconstrainedCubeSolver(RubikCubeSolver):
"""
Generates a series of goals to solve a Rubik's cube.
Goals are not constrained to apply to a particular face.
"""
def __init__(
self,
mujoco_simulation,
success_threshold: typing.Dict[str, float],
face_geom_names: typing.List[str],
num_scramble_steps: int,
):
"""
Creates new UnconstrainedCubeSolver object
"""
self.success_threshold = success_threshold
super().__init__(
mujoco_simulation=mujoco_simulation,
face_geom_names=face_geom_names,
num_scramble_steps=num_scramble_steps,
)
def _is_goal_met(self, current_face_state, threshold):
"""
Check if current face state matches current goal state.
"""
face_diff = rotation.normalize_angles(current_face_state - self.goal_face_state)
return np.linalg.norm(face_diff, axis=-1) < threshold
def next_goal(self, random_state, current_state):
""" Generates a new goal from current cube goal state """
cube_pos = current_state["cube_pos"]
cube_quat = current_state["cube_quat"]
cube_face = current_state["cube_face_angle"]
# Success threshold parameters
face_threshold = self.success_threshold["cube_face_angle"]
# Check if current state already meets goal state.
if self._is_goal_met(cube_face, face_threshold):
# Step forward in goal sequence to get next goal.
self._step_goal()
# Directly rotate the face indicated by the goal action.
goal_action = self._get_goal_action()
face_to_shift = goal_action.face_idx
self.mujoco_simulation.target_model.rotate_face(
face_to_shift // 2, face_to_shift % 2, goal_action.face_angle
)
# align cube quat for visualization purposes, has no effect on goal being met
cube_quat = rotation.quat_normalize(rotation.round_to_straight_quat(cube_quat))
return {
"cube_pos": cube_pos,
"cube_quat": cube_quat,
"cube_face_angle": self.goal_face_state,
"goal_type": "rotation",
}
def relative_goal(self, goal_state, current_state):
"""
Calculate a difference in the 'goal space' between current state and the target goal
"""
goal_type = goal_state["goal_type"]
assert goal_type == "rotation", 'unknown goal_type "{}"'.format(goal_type)
return {
# Cube pos does not count
"cube_pos": np.zeros(goal_state["cube_pos"].shape),
# Quaternion difference of a rotation
"cube_quat": np.zeros(goal_state["cube_quat"].shape),
# Angle differences
"cube_face_angle": rotation.normalize_angles(
goal_state["cube_face_angle"] - current_state["cube_face_angle"]
),
}
def goal_distance(self, goal_state, current_state):
""" Distance from the current goal to the target state. """
relative_goal = self.relative_goal(goal_state, current_state)
goal_distance = {
"cube_pos": 0.0,
"cube_quat": 0.0, # qpos has no effect on whether we consider goal achieved
"cube_face_angle": np.linalg.norm(relative_goal["cube_face_angle"]),
"steps_to_solve": len(self.goal_sequence)
- (self.goal_step % len(self.goal_sequence)),
}
return goal_distance
def goal_reachable(self, goal_state, current_state):
""" Check if goal is in reach from current state."""
relative_goal = self.relative_goal(goal_state, current_state)
face_rotation_angles = relative_goal["cube_face_angle"]
goal_type = goal_state["goal_type"]
assert goal_type == "rotation", 'unknown goal_type "{}"'.format(goal_type)
eps = 1e-6
rounded_rotation_angles = rotation.round_to_straight_angles(
np.abs(rotation.normalize_angles(face_rotation_angles))
)
rotated_faces = list(np.where(rounded_rotation_angles > eps)[0])
goal_face_idx = self._get_goal_action().face_idx
return rounded_rotation_angles[
goal_face_idx
] < np.pi / 2 + eps and rotated_faces in ([], [goal_face_idx])
| 4,548 | 35.103175 | 92 | py |
robogym | robogym-master/robogym/envs/dactyl/goals/face_curriculum.py | import typing
import numpy as np
from robogym.envs.dactyl.common import cube_utils
from robogym.goal.goal_generator import GoalGenerator
from robogym.utils import rotation
class FaceCurriculumGoal(GoalGenerator):
""" 'Face curriculum' goal generation. Generate goals that specify a fully aligned cube at a
desired orientation with the specified face being up.
"""
def __init__(
self,
mujoco_simulation,
success_threshold: dict,
face_geom_names: typing.List[str],
goal_directions: typing.Optional[typing.List[str]] = None,
round_target_face: bool = True,
p_face_flip: float = 0.25,
):
"""
Create new FaceCurriculumGoal object
:param mujoco_simulation: A SimulationInterface object for a mujoco simulation considered
:param success_threshold: Dictionary of threshold levels for cube orientation and face
rotation, for which we consider the cube "aligned" with the goal
:param face_geom_names: Names of 6 face geoms of the cube for which we measure the rotation
:param goal_directions: Whether to rotate faces only clockwise, counterclockwise or both
:param round_target_face: Whether target face rotations should be only round angles
(multiplies of pi/2) or not
:param p_face_flip: If the cube is aligned, what is the probability of flipping the cube
vs rotating the face
"""
super().__init__()
assert len(face_geom_names) in {2, 6}, "Only supports full cube or face cube"
self.mujoco_simulation = mujoco_simulation
self.success_threshold = success_threshold
self.face_geom_names = face_geom_names
if goal_directions is None:
self.goal_directions = ["cw", "ccw"]
else:
self.goal_directions = goal_directions
self.round_target_face = round_target_face
self.p_face_flip = p_face_flip
self.goal_quat_for_face = cube_utils.face_up_quats(
mujoco_simulation.sim, "cube:cube:rot", self.face_geom_names
)
def next_goal(self, random_state, current_state):
""" Generate a new goal from current cube goal state """
cube_pos = current_state["cube_pos"]
cube_quat = current_state["cube_quat"]
cube_face = current_state["cube_face_angle"]
# Success threshold parameters
face_threshold = self.success_threshold["cube_face_angle"]
rot_threshold = self.success_threshold["cube_quat"]
self.mujoco_simulation.clone_target_from_cube()
self.mujoco_simulation.align_target_faces()
rounded_current_face = rotation.round_to_straight_angles(cube_face)
# Face aligned - are faces in the current cube aligned within the threshold
current_face_diff = rotation.normalize_angles(cube_face - rounded_current_face)
face_aligned = np.linalg.norm(current_face_diff, axis=-1) < face_threshold
# Z aligned - is there a cube face looking up within the rotation threshold
if len(self.face_geom_names) == 2:
z_aligned = rotation.rot_z_aligned(cube_quat, rot_threshold)
else: # len(self.face_geom_names) == 6
z_aligned = rotation.rot_xyz_aligned(cube_quat, rot_threshold)
# Do reorientation - with some probability, just reorient the cube
do_reorientation = random_state.uniform() < self.p_face_flip
# Rotate face - should we rotate face or reorient the cube
rotate_face = face_aligned and z_aligned and not do_reorientation
if rotate_face:
# Chose index from the geoms that is highest on the z axis
face_to_shift = cube_utils.face_up(
self.mujoco_simulation.sim, self.face_geom_names
)
# Rotate given face by a random angle and return both, new rotations and an angle
goal_face, delta_angle = cube_utils.rotated_face_with_angle(
cube_face,
face_to_shift,
random_state,
self.round_target_face,
directions=self.goal_directions,
)
if len(self.face_geom_names) == 2:
self.mujoco_simulation.rotate_target_face(face_to_shift, delta_angle)
else:
self.mujoco_simulation.rotate_target_face(
face_to_shift // 2, face_to_shift % 2, delta_angle
)
goal_quat = rotation.round_to_straight_quat(cube_quat)
else: # need to flip cube
# Gaol for face rotations is just aligning them
goal_face = rounded_current_face
# Make the goal so that a given face is straight up
candidates = list(range(len(self.face_geom_names)))
face_to_shift = random_state.choice(candidates)
z_quat = cube_utils.uniform_z_aligned_quat(random_state)
face_up_quat = self.goal_quat_for_face[face_to_shift]
goal_quat = rotation.quat_mul(z_quat, face_up_quat)
goal_quat = rotation.quat_normalize(goal_quat)
return {
"cube_pos": cube_pos,
"cube_quat": goal_quat,
"cube_face_angle": goal_face,
"goal_type": "rotation" if rotate_face else "flip",
}
def current_state(self):
""" Extract current cube goal state """
cube_pos = np.zeros(3)
return {
"cube_pos": cube_pos,
"cube_quat": self.mujoco_simulation.get_qpos("cube_rotation"),
"cube_face_angle": self.mujoco_simulation.get_face_angles("cube"),
}
def relative_goal(self, goal_state, current_state):
"""
Calculate a difference in the 'goal space' between current state and the target goal
"""
return {
# Cube pos does not count
"cube_pos": np.zeros(goal_state["cube_pos"].shape),
# Quaternion difference of a rotation
"cube_quat": rotation.quat_difference(
goal_state["cube_quat"], current_state["cube_quat"]
),
# Angle differences
"cube_face_angle": rotation.normalize_angles(
goal_state["cube_face_angle"] - current_state["cube_face_angle"]
),
}
def goal_distance(self, goal_state, current_state):
""" Distance from the current goal to the target state. """
relative_goal = self.relative_goal(goal_state, current_state)
goal_distance = {
"cube_pos": 0.0,
"cube_quat": rotation.quat_magnitude(relative_goal["cube_quat"]),
"cube_face_angle": np.linalg.norm(relative_goal["cube_face_angle"]),
}
return goal_distance
def goal_types(self) -> typing.Set[str]:
return {"rotation", "flip"}
| 6,870 | 38.262857 | 99 | py |
robogym | robogym-master/robogym/envs/dactyl/goals/release_cube_solver.py | import logging
from robogym.envs.dactyl.goals.face_cube_solver import FaceCubeSolverGoal
logger = logging.getLogger(__name__)
class ReleaseCubeSolverGoal(FaceCubeSolverGoal):
def face_threshold(self):
"""
Dynamic face threshold to use a custom success threshold
that is lower than the typical threshold
to assess face alignment once the cube has been fully solved
:return:
"""
if self.goal_step < len(self.goal_sequence):
return self.success_threshold["cube_face_angle"]
return 0.05
def _get_goal_action(self):
"""
Get the required action to achieve current goal state.
"""
# We solve the cube once and stop generating goals.
if self.goal_step < len(self.goal_sequence):
goal = self.goal_sequence[self.goal_step]
else:
goal = self.goal_sequence[-1]
self.reached_terminal_state = True
return goal
| 977 | 30.548387 | 73 | py |
robogym | robogym-master/robogym/envs/dactyl/goals/locked_parallel.py | from typing import Set
import numpy as np
from numpy.random import RandomState
from robogym.envs.dactyl.common import cube_utils
from robogym.envs.dactyl.common.cube_env import CubeSimulationInterface
from robogym.goal.goal_generator import GoalGenerator
from robogym.utils import rotation
class LockedParallelGoal(GoalGenerator):
"""
Generates random orientation goals for the locked cube. Specifically, the goal
orientation always has the sides aligned with, or "parallel" with, the x-y-z axes.
Hence the name "Parallel" for this goal generator.
Note: historically, we've also called this goal generator "XYZ," so you might see
that name mentioned in docs etc.
"""
def __init__(self, mujoco_simulation: CubeSimulationInterface):
"""
Create new FaceCubeSolverGoalGenerator object
:param success_threshold: Dictionary of threshold levels for cube orientation and face
rotation, for which we consider the cube "aligned" with the goal
"""
self.mujoco_simulation = mujoco_simulation
super().__init__()
def next_goal(self, random_state: RandomState, current_state: dict) -> dict:
""" Generate a new goal from current cube goal state """
# we just sample a random orientation, so current goal_state isn't used
z_quat = cube_utils.uniform_z_aligned_quat(random_state)
quat_choice = random_state.randint(len(cube_utils.PARALLEL_QUATS))
parallel_quat = cube_utils.PARALLEL_QUATS[quat_choice]
goal_quat = rotation.quat_mul(z_quat, parallel_quat)
# Create qpos for goal state (with just cube quat set) for rendering purposes.
qpos_goal = np.zeros_like(self.mujoco_simulation.qpos)
qpos_inds = self.mujoco_simulation.qpos_idxs["cube_rotation"]
qpos_goal[qpos_inds] = goal_quat
qpos_pos_inds = self.mujoco_simulation.qpos_idxs["cube_position"]
qpos_goal[qpos_pos_inds] = np.array([0.0, 0.0, -0.025])
return {"cube_quat": goal_quat, "qpos_goal": qpos_goal, "goal_type": "flip"}
def current_state(self) -> dict:
""" Extract current cube goal state """
return {
"cube_quat": self.mujoco_simulation.get_qpos("cube_rotation"),
}
def relative_goal(self, goal_state: dict, current_state: dict) -> dict:
"""
Calculate a difference in the 'goal space' between current state and the target goal
"""
return {
# We don't care about pos in goal. But we have to include it here because
# we need cube pos to be present in relative_goal observation.
"cube_pos": np.zeros(3),
# Quaternion difference of a rotation
"cube_quat": rotation.quat_difference(
goal_state["cube_quat"], current_state["cube_quat"]
),
}
def goal_distance(self, goal_state: dict, current_state: dict) -> dict:
""" Distance from the current goal to the target state. """
relative_goal = self.relative_goal(goal_state, current_state)
goal_distance = {
"cube_quat": rotation.quat_magnitude(relative_goal["cube_quat"]),
}
return goal_distance
def goal_types(self) -> Set[str]:
return {"flip"}
| 3,288 | 40.1125 | 94 | py |
robogym | robogym-master/robogym/envs/dactyl/goals/face_free.py | import typing
import numpy as np
from robogym.envs.dactyl.common import cube_utils
from robogym.goal.goal_generator import GoalGenerator
from robogym.utils import rotation
class FaceFreeGoal(GoalGenerator):
"""
Rotate the top face of the cube and make sure it's still a top face, but don't constrain
the rotation of the "up" (Z) axis, allowing the cube to have any orientation with the
desired top face.
"""
def __init__(
self,
mujoco_simulation,
success_threshold: dict,
face_geom_names: typing.List[str],
goal_directions: typing.Optional[typing.List[str]] = None,
round_target_face: bool = True,
p_face_flip: float = 0.25,
):
"""
Create new FaceFreeGoal object
:param mujoco_simulation: A SimulationInterface object for a mujoco simulation considered
:param success_threshold: Dictionary of threshold levels for cube orientation and face
rotation, for which we consider the cube "aligned" with the goal
:param face_geom_names: Names of 6 face geoms of the cube for which we measure the rotation
:param goal_directions: Whether to rotate faces only clockwise, counterclockwise or both
:param round_target_face: Whether target face rotations should be only round angles
(multiplies of pi/2) or not
:param p_face_flip: If the cube is aligned, what is the probability of flipping the cube
vs rotating the face
"""
super().__init__()
assert len(face_geom_names) in {2, 6}, "Only supports full cube or face cube"
self.mujoco_simulation = mujoco_simulation
self.success_threshold = success_threshold
self.face_geom_names = face_geom_names
if goal_directions is None:
self.goal_directions = ["cw", "ccw"]
else:
self.goal_directions = goal_directions
self.round_target_face = round_target_face
self.p_face_flip = p_face_flip
self.goal_quat_for_face = cube_utils.face_up_quats(
mujoco_simulation.sim, "cube:cube:rot", self.face_geom_names
)
def next_goal(self, random_state, current_state):
""" Generate a new goal from current cube goal state """
cube_quat = current_state["cube_quat"]
cube_face = current_state["cube_face_angle"]
# Success threshold parameters
face_threshold = self.success_threshold["cube_face_angle"]
rot_threshold = self.success_threshold["cube_quat"]
self.mujoco_simulation.clone_target_from_cube()
self.mujoco_simulation.align_target_faces()
rounded_current_face = rotation.round_to_straight_angles(cube_face)
# Face aligned - are faces in the current cube aligned within the threshold
current_face_diff = rotation.normalize_angles(cube_face - rounded_current_face)
face_aligned = np.linalg.norm(current_face_diff) < face_threshold
# Z aligned - is there a cube face looking up within the rotation threshold
if len(self.face_geom_names) == 2:
z_aligned = rotation.rot_z_aligned(cube_quat, rot_threshold)
else: # len(self.face_geom_names) == 6
z_aligned = rotation.rot_xyz_aligned(cube_quat, rot_threshold)
axis_nr, axis_sign = cube_utils.up_axis_with_sign(cube_quat)
# Do reorientation - with some probability, just reorient the cube
do_reorientation = random_state.uniform() < self.p_face_flip
# Rotate face - should we rotate face or reorient the cube
rotate_face = face_aligned and z_aligned and not do_reorientation
if rotate_face:
# Chose index from the geoms that is highest on the z axis
face_to_shift = cube_utils.face_up(
self.mujoco_simulation.sim, self.face_geom_names
)
# Rotate given face by a random angle and return both, new rotations and an angle
goal_face, delta_angle = cube_utils.rotated_face_with_angle(
cube_face,
face_to_shift,
random_state,
self.round_target_face,
directions=self.goal_directions,
)
if len(self.face_geom_names) == 2:
self.mujoco_simulation.rotate_target_face(face_to_shift, delta_angle)
else:
self.mujoco_simulation.rotate_target_face(
face_to_shift // 2, face_to_shift % 2, delta_angle
)
goal_quat = cube_utils.align_quat_up(cube_quat)
else: # need to flip cube
# Gaol for face rotations is just aligning them
goal_face = rounded_current_face
# Make the goal so that a given face is straight up
candidates = list(range(len(self.face_geom_names)))
face_to_shift = random_state.choice(candidates)
z_quat = cube_utils.uniform_z_aligned_quat(random_state)
face_up_quat = self.goal_quat_for_face[face_to_shift]
goal_quat = rotation.quat_mul(z_quat, face_up_quat)
goal_quat = rotation.quat_normalize(goal_quat)
return {
"cube_pos": np.zeros(3),
"cube_quat": goal_quat,
"cube_face_angle": goal_face,
"goal_type": "rotation" if rotate_face else "flip",
"axis_nr": axis_nr,
"axis_sign": axis_sign,
}
def current_state(self):
""" Extract current cube goal state """
return {
"cube_pos": self.mujoco_simulation.get_qpos("cube_position"),
"cube_quat": self.mujoco_simulation.get_qpos("cube_rotation"),
"cube_face_angle": self.mujoco_simulation.get_face_angles("cube"),
}
def relative_goal(self, goal_state, current_state):
"""
Calculate a difference in the 'goal space' between current state and the target goal
"""
if goal_state["goal_type"] == "rotation":
orientation_distance = cube_utils.distance_quat_from_being_up(
current_state["cube_quat"],
goal_state["axis_nr"],
goal_state["axis_sign"],
)
elif goal_state["goal_type"] == "flip":
orientation_distance = rotation.quat_difference(
goal_state["cube_quat"], current_state["cube_quat"]
)
else:
raise ValueError('unknown goal_type "{}"'.format(goal_state["goal_type"]))
return {
# Cube pos does not count
"cube_pos": np.zeros(goal_state["cube_pos"].shape),
# Quaternion difference of a rotation
"cube_quat": orientation_distance,
# Angle differences
"cube_face_angle": rotation.normalize_angles(
goal_state["cube_face_angle"] - current_state["cube_face_angle"]
),
}
def goal_distance(self, goal_state, current_state):
""" Distance from the current goal to the target state. """
relative_goal = self.relative_goal(goal_state, current_state)
goal_distance = {
"cube_pos": 0.0,
"cube_quat": rotation.quat_magnitude(relative_goal["cube_quat"]),
"cube_face_angle": np.linalg.norm(relative_goal["cube_face_angle"]),
}
return goal_distance
def goal_types(self) -> typing.Set[str]:
"""See parent for documentation."""
return {"rotation", "flip"}
| 7,517 | 38.568421 | 99 | py |
robogym | robogym-master/robogym/envs/dactyl/goals/locked_real_image.py | import numpy as np
from numpy.random import RandomState
from robogym.envs.dactyl.common.cube_env import CubeSimulationInterface
from robogym.envs.dactyl.common.cube_utils import DEFAULT_CAMERA_NAMES
from robogym.envs.dactyl.goals.locked_parallel import LockedParallelGoal
class LockedRealImageGoal(LockedParallelGoal):
"""
Goal generation which uses iterate through a sequence of goal images loaded
from disk.
"""
def __init__(self, mujoco_simulation: CubeSimulationInterface, goal_data_path: str):
super().__init__(mujoco_simulation)
self.goals = np.load(goal_data_path)
self.goal_idx = 0
def next_goal(self, random_state: RandomState, goal_state: dict) -> dict:
"""
Load next goal image.
"""
num_goals = len(self.goals["quats"])
goal_image = np.concatenate(
[
self.goals[cam][self.goal_idx % num_goals]
for cam in DEFAULT_CAMERA_NAMES
],
axis=0,
)
goal = {
"cube_quat": self.goals["quats"][self.goal_idx % num_goals],
"qpos_goal": np.zeros_like(self.mujoco_simulation.qpos),
"vision_goal": goal_image,
"goal_type": "flip",
}
self.goal_idx += 1
return goal
| 1,309 | 30.190476 | 88 | py |
robogym | robogym-master/robogym/envs/dactyl/goals/shadow_hand_reach_fingertip_pos.py | import numpy as np
from numpy.random import RandomState
from robogym.envs.dactyl.reach import ReachSimulation
from robogym.goal.goal_generator import GoalGenerator
from robogym.robot.shadow_hand.hand_forward_kinematics import FINGERTIP_SITE_NAMES
from robogym.utils.dactyl_utils import actuated_joint_range
class FingertipPosGoal(GoalGenerator):
"""
Goal generation to sample random qpos within actuator control range.
"""
def __init__(
self, mujoco_simulation: ReachSimulation, goal_simulation: ReachSimulation
):
"""
Create new FingertipPosGoal object
"""
self.mujoco_simulation = mujoco_simulation
self.goal_simulation = goal_simulation
self.goal_joint_pos = mujoco_simulation.shadow_hand.observe().joint_positions()
super().__init__()
def next_goal(self, random_state: RandomState, current_state: dict) -> dict:
"""
Goal is defined as fingertip position.
We sample next goal by sampling actuator control within control range then use
forward kinematic to calculate fingertip position.
"""
sim = self.mujoco_simulation.mj_sim
goal_sim = self.goal_simulation.mj_sim
# We need to update control range and joint range for goal simulation because
# they can be changed by randomizers.
goal_sim.model.jnt_range[:] = sim.model.jnt_range
goal_sim.model.actuator_ctrlrange[:] = sim.model.actuator_ctrlrange
# Sample around current pose of the fingers in joint space.
joint_limits = actuated_joint_range(sim)
joint_range = joint_limits[:, 1] - joint_limits[:, 0]
goal_joint_pos = random_state.normal(
loc=self.goal_joint_pos, scale=0.1 * joint_range
)
goal_joint_pos = np.clip(goal_joint_pos, joint_limits[:, 0], joint_limits[:, 1])
# replace state to ensure reachability with current model
self.goal_simulation.set_qpos("robot0:hand_joint_angles", goal_joint_pos)
self.goal_simulation.forward()
# take a few steps to avoid goals that are impossible due to contacts
for steps in range(2):
self.goal_simulation.shadow_hand.set_position_control(
self.goal_simulation.shadow_hand.denormalize_position_control(
self.goal_simulation.shadow_hand.zero_control(),
relative_action=True,
)
)
self.goal_simulation.step()
self.goal_joint_pos = (
self.goal_simulation.shadow_hand.observe().joint_positions()
)
return {
"fingertip_pos": self._get_fingertip_position(self.goal_simulation),
}
def current_state(self) -> dict:
""" Extract current cube goal state """
return {"fingertip_pos": self._get_fingertip_position(self.mujoco_simulation)}
def relative_goal(self, goal_state: dict, current_state: dict) -> dict:
return {
"fingertip_pos": goal_state["fingertip_pos"]
- current_state["fingertip_pos"]
}
def goal_distance(self, goal_state: dict, current_state: dict) -> dict:
relative_goal = self.relative_goal(goal_state, current_state)
return {"fingertip_pos": np.linalg.norm(relative_goal["fingertip_pos"])}
@staticmethod
def _get_fingertip_position(simulation: ReachSimulation):
"""
Get absolute fingertip positions in mujoco frame.
"""
fingertip_pos = np.array(
[
simulation.mj_sim.data.get_site_xpos(f"robot0:{site}")
for site in FINGERTIP_SITE_NAMES
]
)
fingertip_pos = fingertip_pos.flatten()
return fingertip_pos
| 3,768 | 35.240385 | 88 | py |
robogym | robogym-master/robogym/envs/dactyl/goals/face_cube_solver.py | import logging
import typing
import numpy as np
from robogym.envs.dactyl.common import cube_utils
from robogym.envs.dactyl.goals.rubik_cube_solver import RubikCubeSolver
from robogym.utils import rotation
logger = logging.getLogger(__name__)
class FaceCubeSolverGoal(RubikCubeSolver):
"""
Generates a series of goals to solve a Rubik's cube.
Goals are generated in a way to always rotate the top face.
"""
def __init__(
self,
mujoco_simulation,
success_threshold: typing.Dict[str, float],
face_geom_names: typing.List[str],
num_scramble_steps: int,
):
"""
Create new FaceCubeSolverGoalGenerator object
:param success_threshold: Dictionary of threshold levels for cube orientation and face
rotation, for which we consider the cube "aligned" with the goal
"""
self.success_threshold = success_threshold
super().__init__(
mujoco_simulation=mujoco_simulation,
face_geom_names=face_geom_names,
num_scramble_steps=num_scramble_steps,
)
def _is_goal_met(self, current_face_state, threshold):
"""
Check if current face state matches current goal state.
"""
face_up = cube_utils.face_up(self.mujoco_simulation.sim, self.face_geom_names)
goal_face_idx = self._get_goal_action().face_idx
face_diff = rotation.normalize_angles(current_face_state - self.goal_face_state)
return (
face_up == goal_face_idx and np.linalg.norm(face_diff, axis=-1) < threshold
)
def face_threshold(self):
return self.success_threshold["cube_face_angle"]
def next_goal(self, random_state, current_state):
""" Generate a new goal from current cube goal state """
cube_pos = current_state["cube_pos"]
cube_quat = current_state["cube_quat"]
cube_face = current_state["cube_face_angle"]
# Success threshold parameters
face_threshold = self.face_threshold()
rot_threshold = self.success_threshold["cube_quat"]
rounded_current_face = rotation.round_to_straight_angles(cube_face)
# Face aligned - are faces in the current cube aligned within the threshold
current_face_diff = rotation.normalize_angles(cube_face - rounded_current_face)
face_aligned = np.linalg.norm(current_face_diff, axis=-1) < face_threshold
# Z aligned - is there a cube face looking up within the rotation threshold
z_aligned = rotation.rot_xyz_aligned(cube_quat, rot_threshold)
axis_nr, axis_sign = cube_utils.up_axis_with_sign(cube_quat)
cube_aligned = face_aligned and z_aligned
# Check if current state already meets goal state.
if cube_aligned and self._is_goal_met(cube_face, face_threshold):
# Step forward in goal sequence to get next goal.
self._step_goal()
goal_action = self._get_goal_action()
if cube_aligned:
# Choose index from the geoms that is highest on the z axis
face_to_shift = cube_utils.face_up(
self.mujoco_simulation.sim, self.face_geom_names
)
# Rotate face if the face to rotate for next goal is facing up.
rotate_face = face_to_shift == goal_action.face_idx
else:
rotate_face = False
if rotate_face:
self.mujoco_simulation.target_model.rotate_face(
face_to_shift // 2, face_to_shift % 2, goal_action.face_angle
)
goal_quat = cube_utils.align_quat_up(cube_quat)
goal_face = self.goal_face_state
else: # need to flip cube
# Rotate cube so that goal face is on the top. We currently apply
# a deterministic transformation here that would get the goal face to the top,
# which is _not_ the minimal possible orientation change, which may be
# worth addressing in the future.
goal_quat = self.goal_quat_for_face[goal_action.face_idx]
# No need to rotate face, just align them.
goal_face = rounded_current_face
goal_quat = rotation.quat_normalize(goal_quat)
return {
"cube_pos": cube_pos,
"cube_quat": goal_quat,
"cube_face_angle": goal_face,
"goal_type": "rotation" if rotate_face else "flip",
"axis_nr": axis_nr,
"axis_sign": axis_sign,
}
def relative_goal(self, goal_state, current_state):
"""
Calculate a difference in the 'goal space' between current state and the target goal
"""
if goal_state["goal_type"] == "rotation":
orientation_distance = cube_utils.distance_quat_from_being_up(
current_state["cube_quat"],
goal_state["axis_nr"],
goal_state["axis_sign"],
)
elif goal_state["goal_type"] == "flip":
orientation_distance = rotation.quat_difference(
goal_state["cube_quat"], current_state["cube_quat"]
)
else:
raise ValueError('unknown goal_type "{}"'.format(goal_state["goal_type"]))
return {
# Cube pos does not count
"cube_pos": np.zeros(goal_state["cube_pos"].shape),
# Quaternion difference of a rotation
"cube_quat": orientation_distance,
# Angle differences
"cube_face_angle": rotation.normalize_angles(
goal_state["cube_face_angle"] - current_state["cube_face_angle"]
),
}
def goal_distance(self, goal_state, current_state):
""" Distance from the current goal to the target state. """
relative_goal = self.relative_goal(goal_state, current_state)
goal_distance = {
"cube_pos": 0.0,
"cube_quat": rotation.quat_magnitude(relative_goal["cube_quat"]),
"cube_face_angle": np.linalg.norm(relative_goal["cube_face_angle"]),
"steps_to_solve": len(self.goal_sequence)
- (self.goal_step % len(self.goal_sequence)),
"goal_step": self.goal_step,
}
return goal_distance
def goal_reachable(self, goal_state, current_state):
""" Check if goal is in reach from current state."""
relative_goal = self.relative_goal(goal_state, current_state)
face_rotation_angles = relative_goal["cube_face_angle"]
goal_type = goal_state["goal_type"]
eps = 1e-6
rounded_rotation_angles = rotation.round_to_straight_angles(
np.abs(rotation.normalize_angles(face_rotation_angles))
)
rotated_faces = list(np.where(rounded_rotation_angles > eps)[0])
if goal_type == "rotation":
# When doing face rotation, three conditions should met:
# 1. Goal face should face up
# 2. Rounded rotation angle for goal face should be at most 90 degree.
# 3. Rounded rotation angle for other faces should be 0.
goal_face_idx = self._get_goal_action().face_idx
face_up = cube_utils.face_up(
self.mujoco_simulation.sim, self.face_geom_names
)
return (
goal_face_idx == face_up
and rounded_rotation_angles[goal_face_idx] < np.pi / 2 + eps
and rotated_faces in ([], [goal_face_idx])
)
elif goal_type == "flip":
# When doing flipping, rounded rotation angles should be 0.
return len(rotated_faces) == 0
else:
raise ValueError('unknown goal_type "{}"'.format(goal_type))
| 7,728 | 37.645 | 94 | py |
robogym | robogym-master/robogym/envs/dactyl/goals/rubik_cube_solver.py | import logging
import typing
import numpy as np
import pycuber
from robogym.envs.dactyl.common import cube_utils
from robogym.goal.goal_generator import GoalGenerator
from robogym.utils import rotation
from robogym.utils.rubik_utils import solve_fast
logger = logging.getLogger(__name__)
class GoalAction(typing.NamedTuple):
face_idx: int
face_angle: float
pycuber_action: str
num_remaining_actions: int
class RubikCubeSolver(GoalGenerator):
"""
Solve the cube using the kociemba rubik cube solver
"""
# Mapping from Mujoco face id to pycuber action.
FACE_ACTIONS = tuple("LRFBDU")
# Mapping from Mujoco face id to cube color.
FACE_COLORS = tuple("ROGBWY")
# Mapping from Mujoco face id to rotation sign. Note that pycuber
# rotation angle needs to be flipped for certain faces.
FACE_SIGNS = [1, -1, 1, -1, 1, -1]
def __init__(
self,
mujoco_simulation,
face_geom_names: typing.List[str],
num_scramble_steps: int,
):
"""
Create new FaceCubeSolverGoalGenerator object
:param mujoco_simulation: A SimulationInterface object for a mujoco simulation considered
:param success_threshold: Dictionary of threshold levels for cube orientation and face
rotation, for which we consider the cube "aligned" with the goal
"""
super().__init__()
assert len(face_geom_names) == 6, "Only full cube can be solved"
self.mujoco_simulation = mujoco_simulation
self.face_geom_names = face_geom_names
self.num_scramble_steps = num_scramble_steps
self.goal_quat_for_face = cube_utils.face_up_quats(
mujoco_simulation.sim, "cube:cube:rot", self.face_geom_names
)
self._reset_goal_state(pycuber.Cube())
def _generate_solution_sequence(self, cube):
"""
Returns an action sequence to solve the cube.
"""
# Try to find a reasonable length solution.
step = 0
solution = None
while step < 5:
try:
max_depth = self.num_scramble_steps + 2 ** step - 1
solution = solve_fast(cube, max_depth=max_depth)
break
except ValueError:
logging.info(f"Cannot solve cube within {max_depth} steps.")
step += 1
assert solution is not None, f"Could not find solution in {max_depth} steps"
return self._normalize_actions(solution.split())
@classmethod
def _normalize_actions(cls, actions):
"""
Normalize Singmaster Notation to tuple of action in'UDLRFB' and angle in (pi/2, -pi/2).
"""
normalized_actions = []
for i, action in enumerate(actions):
# We track to number of remaining pycuber actions so we can use it to generate
# optimal solution sequence during goal regeneration.
num_actions = 1
num_remaining_actions = len(actions) - i
if action.endswith("2"):
num_actions = 2
action = action[:-1]
if action.endswith("'"):
angle = -np.pi / 2
else:
angle = np.pi / 2
face_idx = cls.FACE_ACTIONS.index(action[0])
angle *= cls.FACE_SIGNS[face_idx]
normalized_actions.extend(
[
GoalAction(
face_idx=face_idx,
face_angle=angle,
pycuber_action=action,
num_remaining_actions=num_remaining_actions,
)
]
* num_actions
)
return normalized_actions
def _step_goal(self):
"""
Move one step forward in the goal sequence. Also update current goal state.
"""
self.goal_step += 1
goal_action = self._get_goal_action()
self.goal_face_state[goal_action.face_idx] += goal_action.face_angle
def _get_goal_action(self):
"""
Get the required action to achieve current goal state.
"""
# We solve the cube first and then reverse this sequence, then solve again, and
# so on until we run out of time.
solve_forward = (self.goal_step // len(self.goal_sequence)) % 2 == 0
if solve_forward:
goal_idx = self.goal_step % len(self.goal_sequence)
goal = self.goal_sequence[goal_idx]
else:
goal_idx = (
len(self.goal_sequence) - 1 - (self.goal_step % len(self.goal_sequence))
)
goal = self.goal_sequence[goal_idx]
# flip direction on face rotation
goal = GoalAction(
face_idx=goal.face_idx,
face_angle=-goal.face_angle,
pycuber_action=goal.pycuber_action,
num_remaining_actions=goal.num_remaining_actions,
)
return goal
def current_state(self):
""" Extract current cube goal state """
cube_pos = self.mujoco_simulation.get_qpos("cube_position")
return {
"cube_pos": cube_pos,
"cube_quat": self.mujoco_simulation.get_qpos("cube_rotation"),
"cube_face_angle": self.mujoco_simulation.get_face_angles("cube"),
}
def reset(self, random_state):
""" Reset state of the goal generator. """
cube = self.mujoco_simulation.cube_model.to_pycuber()
self._reset_goal_state(cube)
def _reset_goal_state(self, cube):
self.reached_terminal_state = False
initial_goal_state = self.current_state()["cube_face_angle"]
initial_goal_state = rotation.round_to_straight_angles(initial_goal_state)
logger.info("Reset goal generation state with pycuber state")
logger.info(cube)
self.mujoco_simulation.clone_target_from_cube()
self.mujoco_simulation.align_target_faces()
self.goal_sequence = self._generate_solution_sequence(cube)
logger.info("Goal Sequence:")
self._print_goal_sequence()
self.goal_face_state = initial_goal_state
self.goal_step = -1
self._step_goal()
def _print_goal_sequence(self):
sequence = " ".join(a.pycuber_action for a in self.goal_sequence)
logging.info(f"Solution Sequence: {sequence}")
def goal_types(self) -> typing.Set[str]:
return {"rotation", "flip"}
| 6,493 | 31.964467 | 97 | py |
robogym | robogym-master/robogym/envs/dactyl/goals/fixed_fair_scramble.py | import logging
from robogym.envs.dactyl.goals.face_cube_solver import FaceCubeSolverGoal
logger = logging.getLogger(__name__)
class FixedFairScrambleGoal(FaceCubeSolverGoal):
"""
Generates a series of goals to apply a "fair scramble" to a fully solved Rubik's cube.
The fair scramble was generated using the WCA app and was not cherry-picked:
https://www.worldcubeassociation.org/regulations/scrambles/
Goals are generated in a way to always rotate the top face.
"""
def _generate_solution_sequence(self, cube):
solution = "L2 U2 R2 B D2 B2 D2 L2 F' D' R B F L U' F D' L2"
return self._normalize_actions(solution.split())
| 675 | 34.578947 | 90 | py |
robogym | robogym-master/robogym/envs/rearrange/composer.py | import attr
from robogym.envs.rearrange.common.base import (
RearrangeEnv,
RearrangeEnvConstants,
RearrangeEnvParameters,
)
from robogym.envs.rearrange.goals.object_state import GoalArgs
from robogym.envs.rearrange.simulation.composer import (
ComposerRearrangeSim,
ComposerRearrangeSimParameters,
)
from robogym.robot_env import build_nested_attr
@attr.s(auto_attribs=True)
class ComposerRearrangeEnvParameters(RearrangeEnvParameters):
simulation_params: ComposerRearrangeSimParameters = build_nested_attr(
ComposerRearrangeSimParameters
)
@attr.s(auto_attribs=True)
class ComposerRearrangeEnvConstants(RearrangeEnvConstants):
goal_args: GoalArgs = build_nested_attr(GoalArgs, default={"stabilize_goal": True})
goal_generation: str = "train"
class ComposerRearrangeEnv(
RearrangeEnv[
ComposerRearrangeEnvParameters,
ComposerRearrangeEnvConstants,
ComposerRearrangeSim,
]
):
def _sample_group_attributes(self, num_groups: int):
attrs_dict = super()._sample_group_attributes(num_groups)
attrs_dict["num_geoms"] = self._random_state.randint(
low=1,
high=self.parameters.simulation_params.num_max_geoms + 1,
size=num_groups,
)
return attrs_dict
make_env = ComposerRearrangeEnv.build
| 1,338 | 26.895833 | 87 | py |
robogym | robogym-master/robogym/envs/rearrange/chessboard.py | import logging
from typing import List
import attr
import numpy as np
from robogym.envs.rearrange.common.mesh import (
MeshRearrangeEnv,
MeshRearrangeEnvConstants,
MeshRearrangeEnvParameters,
MeshRearrangeSimParameters,
)
from robogym.envs.rearrange.common.utils import find_meshes_by_dirname
from robogym.envs.rearrange.goals.object_state_fixed import ObjectFixedStateGoal
from robogym.envs.rearrange.simulation.base import ObjectGroupConfig
from robogym.envs.rearrange.simulation.chessboard import ChessboardRearrangeSim
from robogym.robot_env import build_nested_attr
logger = logging.getLogger(__name__)
CHESS_CHARS = ["rook", "queen", "bishop", "knight"]
@attr.s(auto_attribs=True)
class ChessboardRearrangeEnvParameters(MeshRearrangeEnvParameters):
simulation_params: MeshRearrangeSimParameters = build_nested_attr(
MeshRearrangeSimParameters,
default=dict(num_objects=len(CHESS_CHARS), mesh_scale=0.4),
)
class ChessboardRearrangeEnv(
MeshRearrangeEnv[
ChessboardRearrangeEnvParameters,
MeshRearrangeEnvConstants,
ChessboardRearrangeSim,
]
):
MESH_FILES = find_meshes_by_dirname("chess")
def _sample_random_object_groups(
self, dedupe_objects: bool = False
) -> List[ObjectGroupConfig]:
return super()._sample_random_object_groups(dedupe_objects=True)
def _sample_object_colors(self, num_groups: int):
assert num_groups == 4
return [[0.267, 0.165, 0.133, 1.0]] * num_groups
def _sample_object_meshes(self, num_groups: int):
assert num_groups == 4
return [self.MESH_FILES[name] for name in CHESS_CHARS[:num_groups]]
def _reset(self):
super()._reset()
# Scale chessboard properly
(x, y, _), (w, h, _), z = self.mujoco_simulation.get_table_dimensions()
placement_area = self.mujoco_simulation.get_placement_area()
px = placement_area.offset[0]
py = placement_area.offset[1]
pw = placement_area.size[0]
ph = placement_area.size[1]
# Move board to center of placement area.
sim = self.mujoco_simulation.mj_sim
body_id = sim.model.body_name2id("chessboard")
sim.model.body_pos[body_id][:] = [x - w + px + pw / 2, y - h + py + ph / 2, z]
self.mujoco_simulation.forward()
@classmethod
def build_goal_generation(cls, constants, mujoco_simulation):
pw, ph, _ = mujoco_simulation.get_placement_area().size
sim = mujoco_simulation.mj_sim
geom_id = sim.model.geom_name2id("chessboard")
cw, ch = sim.model.geom_size[geom_id, :2]
num_objects = mujoco_simulation.num_objects
placements = np.zeros((num_objects, 2))
placements[:, 0] = 1 - 1.0 / num_objects / 2
placements[:, 1] = (
np.linspace(
ph / 2 - ch + ch / num_objects,
ph / 2 + ch - ch / num_objects,
num_objects,
)
/ ph
)
return ObjectFixedStateGoal(
mujoco_simulation, args=constants.goal_args, relative_placements=placements
)
make_env = ChessboardRearrangeEnv.build
| 3,177 | 32.104167 | 87 | py |
robogym | robogym-master/robogym/envs/rearrange/ycb_pickandplace.py | from robogym.envs.rearrange.common.base import RearrangeEnvConstants
from robogym.envs.rearrange.goals.pickandplace import PickAndPlaceGoal
from robogym.envs.rearrange.simulation.mesh import MeshRearrangeSim
from robogym.envs.rearrange.ycb import YcbRearrangeEnv
class YcbPickAndPlaceEnv(YcbRearrangeEnv):
@classmethod
def build_goal_generation(
cls, constants: RearrangeEnvConstants, mujoco_simulation: MeshRearrangeSim
):
return PickAndPlaceGoal(mujoco_simulation, constants.goal_args)
make_env = YcbPickAndPlaceEnv.build
| 556 | 33.8125 | 82 | py |
robogym | robogym-master/robogym/envs/rearrange/table_setting.py | import logging
from typing import List
import attr
import numpy as np
from robogym.envs.rearrange.common.mesh import (
MeshRearrangeEnv,
MeshRearrangeEnvConstants,
MeshRearrangeEnvParameters,
MeshRearrangeSimParameters,
)
from robogym.envs.rearrange.goals.object_state_fixed import ObjectFixedStateGoal
from robogym.envs.rearrange.simulation.base import ObjectGroupConfig
from robogym.envs.rearrange.simulation.mesh import MeshRearrangeSim
from robogym.envs.rearrange.ycb import find_ycb_meshes
from robogym.robot_env import build_nested_attr
from robogym.utils.rotation import quat_from_angle_and_axis
logger = logging.getLogger(__name__)
@attr.s(auto_attribs=True)
class TableSettingRearrangeEnvParameters(MeshRearrangeEnvParameters):
simulation_params: MeshRearrangeSimParameters = build_nested_attr(
MeshRearrangeSimParameters, default=dict(num_objects=5)
)
class TableSettingRearrangeEnv(
MeshRearrangeEnv[
TableSettingRearrangeEnvParameters, MeshRearrangeEnvConstants, MeshRearrangeSim,
]
):
MESH_FILES = find_ycb_meshes()
def _sample_random_object_groups(
self, dedupe_objects: bool = False
) -> List[ObjectGroupConfig]:
return super()._sample_random_object_groups(dedupe_objects=True)
def _sample_object_colors(self, num_groups: int):
assert num_groups == 5
return [[0.99, 0.44, 0.35, 1.0]] + [[0.506, 0.675, 0.75, 1.0]] * 4
def _sample_object_size_scales(self, num_groups: int):
assert num_groups == 5
return [0.6, 0.53, 0.63, 0.6, 0.6]
def _sample_object_meshes(self, num_groups: int):
"""Add one plate, 2 forks, 1 spoon and 1 knife."""
return [
self.MESH_FILES[name]
for name in ["029_plate", "030_fork", "030_fork", "032_knife", "031_spoon"]
]
@classmethod
def build_goal_generation(cls, constants, mujoco_simulation):
return ObjectFixedStateGoal(
mujoco_simulation,
args=constants.goal_args,
relative_placements=np.array(
[
[0.6, 0.5], # "029_plate"
[0.6, 0.68], # "030_fork"
[0.6, 0.75], # "030_fork"
[0.6, 0.36], # "032_knife"
[0.6, 0.28], # "031_spoon"
]
),
init_quats=np.array(
[
[1, 0, 0, 0],
[1, 0, 0, 0],
[1, 0, 0, 0],
[1, 0, 0, 0],
# We need to rotate the spoon a little bit counter-clock-wise to be aligned with others.
quat_from_angle_and_axis(0.38, np.array([0, 0, 1.0])),
]
),
)
make_env = TableSettingRearrangeEnv.build
| 2,813 | 32.105882 | 108 | py |
robogym | robogym-master/robogym/envs/rearrange/holdout.py | import os
from typing import Dict, List, Optional, cast
import attr
import numpy as np
from robogym.envs.rearrange.common.base import (
RearrangeEnv,
RearrangeEnvConstants,
RearrangeEnvParameters,
)
from robogym.envs.rearrange.goals.holdout_object_state import (
HoldoutGoalArgs,
HoldoutObjectStateGoal,
)
from robogym.envs.rearrange.holdouts import STATE_DIR
from robogym.envs.rearrange.simulation.base import ObjectGroupConfig
from robogym.envs.rearrange.simulation.holdout import (
HoldoutRearrangeSim,
HoldoutRearrangeSimParameters,
)
from robogym.robot_env import build_nested_attr
@attr.s(auto_attribs=True)
class HoldoutRearrangeEnvConstants(RearrangeEnvConstants):
# Path to file storing initial state of objects.
# If not specified, initial state will be randomly sampled.
initial_state_path: Optional[str] = None
goal_args: HoldoutGoalArgs = build_nested_attr(HoldoutGoalArgs)
randomize_target: bool = False
@attr.s(auto_attribs=True)
class HoldoutRearrangeEnvParameters(RearrangeEnvParameters):
simulation_params: HoldoutRearrangeSimParameters = build_nested_attr(
HoldoutRearrangeSimParameters
)
# Hold out arg should use explicitly defined material without randomization.
material_names: Optional[List[str]] = attr.ib(default=cast(List[str], []))
@material_names.validator
def validate_material_names(self, _, value):
assert not value, (
"Specifying material names for holdout in parameters is not supported. "
"Please specify material in jsonnet config directly."
)
class HoldoutRearrangeEnv(
RearrangeEnv[
HoldoutRearrangeEnvParameters,
HoldoutRearrangeEnvConstants,
HoldoutRearrangeSim,
]
):
def _sample_random_object_groups(
self, dedupe_objects: bool = False
) -> List[ObjectGroupConfig]:
# Create dummy object groups based on task object config so that reward
# function can take duplicated objects in holdouts into consideration.
object_groups = []
num_objects = self.parameters.simulation_params.num_objects
object_id = 0
for c in self.parameters.simulation_params.task_object_configs[:num_objects]:
object_group = ObjectGroupConfig(count=c.count)
# Set up object ids
object_group.object_ids = list(range(object_id, object_id + c.count))
object_id += c.count
object_groups.append(object_group)
return object_groups
def _sample_group_attributes(self, num_groups: int) -> Dict[str, list]:
# We don't set random attributes for object groups
return {}
def _apply_object_colors(self):
# We don't apply customized object colors.
pass
def _apply_object_size_scales(self):
# We don't apply customized object size scaling.
pass
def _randomize_object_initial_states(self):
if self.constants.initial_state_path:
initial_state = np.load(
os.path.join(STATE_DIR, self.constants.initial_state_path)
)
self.mujoco_simulation.set_object_pos(
initial_state["obj_pos"][: self.mujoco_simulation.num_objects]
)
self.mujoco_simulation.set_object_quat(
initial_state["obj_quat"][: self.mujoco_simulation.num_objects]
)
self.mujoco_simulation.forward()
else:
super()._randomize_object_initial_states()
@classmethod
def build_goal_generation(cls, constants, mujoco_simulation):
if constants.randomize_target:
return super().build_goal_generation(constants, mujoco_simulation)
else:
return HoldoutObjectStateGoal(mujoco_simulation, args=constants.goal_args)
make_env = HoldoutRearrangeEnv.build
| 3,871 | 32.094017 | 86 | py |
robogym | robogym-master/robogym/envs/rearrange/mixture.py | from typing import Any, Dict, List
import attr
from robogym.envs.rearrange.common.mesh import (
MeshRearrangeEnv,
MeshRearrangeEnvConstants,
MeshRearrangeEnvParameters,
)
from robogym.envs.rearrange.datasets.envstates.utils import get_envstate_datasets
from robogym.envs.rearrange.datasets.objects.utils import get_object_datasets
from robogym.envs.rearrange.simulation.base import ObjectGroupConfig
from robogym.envs.rearrange.simulation.mesh import MeshRearrangeSim
@attr.s(auto_attribs=True)
class MixtureRearrangeEnvConstants(MeshRearrangeEnvConstants):
# Set of object datasets used for constructing environment states.
# {object dataset name: object dataset config}
object_config: Dict[str, Dict[str, Any]] = {
"ycb": {
"function": "robogym.envs.rearrange.datasets.objects.local_mesh:create",
"args": {"mesh_dirname": "ycb"},
},
"geom": {
"function": "robogym.envs.rearrange.datasets.objects.local_mesh:create",
"args": {"mesh_dirname": "geom"},
},
}
# Sef of environment state datasets that are used to sample environment states. The
# environment state datasets use object datasets defined by object_config.
# {envstate dataset name: dataset config}
dataset_config: Dict[str, Dict[str, Any]] = {
"ycb_dataset": {
"function": "robogym.envs.rearrange.datasets.envstates.random:create",
"args": {"object_sample_prob": {"ycb": 1.0}},
},
"geom_dataset": {
"function": "robogym.envs.rearrange.datasets.envstates.random:create",
"args": {"object_sample_prob": {"geom": 1.0}},
},
"mixed_dataset": {
"function": "robogym.envs.rearrange.datasets.envstates.random:create",
"args": {"object_sample_prob": {"ycb": 0.5, "geom": 0.5}},
},
}
# environment state dataset level sampling probability.
# {envstate dataset name: probability of sampling from this dataset}
dataset_sampling_config: dict = {
"ycb_dataset": 0.3,
"geom_dataset": 0.3,
"mixed_dataset": 0.4,
}
class MixtureRearrangeEnv(
MeshRearrangeEnv[
MeshRearrangeEnvParameters, MixtureRearrangeEnvConstants, MeshRearrangeSim
]
):
"""
Rearrange environment using mixture of dataset to define an initial state distribution.
"""
def initialize(self):
super().initialize()
self.object_datasets = get_object_datasets(
self.constants.object_config, self._random_state
)
self.datasets = get_envstate_datasets(
self.constants.dataset_config, self.object_datasets, self._random_state
)
# Name of environment state datasets
self.dataset_ids = sorted(list(self.constants.dataset_sampling_config))
# Probability for sampling from each environment state datasets
self.dataset_prob = [
self.constants.dataset_sampling_config[dataset_id]
for dataset_id in self.dataset_ids
]
# Dataset that will be used for sampling an environment state
self.cur_dataset = self._sample_dataset()
def _sample_dataset(self):
""" Sample an environment state dataset """
dataset_id = self.dataset_ids[
self._random_state.choice(len(self.dataset_ids), p=self.dataset_prob)
]
return self.datasets[dataset_id]
def _reset(self):
""" Reset environment state
This function resets envstate dataset and then use the dataset state for resetting the
environment. environment state dataset may randomize on the fly or load previously saved
environment states from storage.
"""
self.cur_dataset = self._sample_dataset()
self.cur_dataset.reset(self)
super()._reset()
#######################################################################################
# Override envstate randomization function in environment to make the dataset fully
# determine the environment states
def _sample_attributed_object_groups(
self, dedupe_objects: bool = False
) -> List[ObjectGroupConfig]:
""" This function includes sampling mesh, scales, and colors """
assert not dedupe_objects, "Mixture dataset always supports duplicated objects"
return self.cur_dataset.envstate.object_groups
def _sample_object_meshes(self, num_groups: int) -> List[List[str]]:
# This function is not necessary because we will directly return object groups
pass
def _generate_object_placements(self):
self.cur_dataset.check_initialized()
return self.cur_dataset.envstate.init_pos, self.cur_dataset.envstate.is_valid
def _sample_object_initial_rotations(self):
self.cur_dataset.check_initialized()
return self.cur_dataset.envstate.init_quats
make_env = MixtureRearrangeEnv.build
| 4,957 | 36.560606 | 96 | py |
robogym | robogym-master/robogym/envs/rearrange/blocks_reach.py | import attr
import numpy as np
from robogym.envs.rearrange.blocks import BlockRearrangeEnvParameters, BlockRearrangeSim
from robogym.envs.rearrange.common.base import RearrangeEnv, RearrangeEnvConstants
from robogym.envs.rearrange.goals.object_reach_goal import (
DeterministicReachGoal,
ObjectReachGoal,
)
@attr.s(auto_attribs=True)
class BlocksReachEnvConstants(RearrangeEnvConstants):
# Goal generation for env.
# det-state: Use deterministic goals
# state: Use random state goals
goal_generation: str = attr.ib(
default="state", validator=attr.validators.in_(["state", "det-state"])
)
class BlocksReachEnv(
RearrangeEnv[
BlockRearrangeEnvParameters, BlocksReachEnvConstants, BlockRearrangeSim,
]
):
@classmethod
def build_goal_generation(cls, constants, mujoco_simulation):
if constants.goal_generation == "det-state":
return DeterministicReachGoal(mujoco_simulation, args=constants.goal_args)
else:
return ObjectReachGoal(mujoco_simulation, args=constants.goal_args)
def _calculate_goal_distance_reward(self, previous_goal_distance, goal_distance):
return np.sum(previous_goal_distance["obj_pos"] - goal_distance["obj_pos"])
make_env = BlocksReachEnv.build
| 1,282 | 31.897436 | 88 | py |
robogym | robogym-master/robogym/envs/rearrange/blocks_stack.py | import logging
import attr
from robogym.envs.rearrange.common.base import (
RearrangeEnv,
RearrangeEnvConstants,
RearrangeEnvParameters,
)
from robogym.envs.rearrange.goals.object_stack_goal import ObjectStackGoal
from robogym.envs.rearrange.simulation.base import RearrangeSimParameters
from robogym.envs.rearrange.simulation.blocks import (
BlockRearrangeSim,
BlockRearrangeSimParameters,
)
from robogym.robot_env import build_nested_attr
logger = logging.getLogger(__name__)
@attr.s(auto_attribs=True)
class BlockStackEnvConstants(RearrangeEnvConstants):
# whether block stacked in the fixed order or random order.
fixed_order: bool = False
@attr.s(auto_attribs=True)
class BlockStackEnvParameters(RearrangeEnvParameters):
simulation_params: RearrangeSimParameters = build_nested_attr(
BlockRearrangeSimParameters, default=dict(num_objects=2)
)
class BlockStackEnv(
RearrangeEnv[BlockStackEnvParameters, BlockStackEnvConstants, BlockRearrangeSim]
):
@classmethod
def build_goal_generation(cls, constants, mujoco_simulation):
return ObjectStackGoal(
mujoco_simulation, constants.goal_args, constants.fixed_order
)
make_env = BlockStackEnv.build
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