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utils.py
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utils.py
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import matplotlib.colors as colors
import os
import tempfile
import numpy as np
import torch
import math
from torch import __init__
from torch import nn
from tqdm import tqdm
# ----------------- model utils -----------------------------------------------------
class DebugModule(nn.Module):
"""
Wrapper class for printing the activation dimensions
"""
def __init__(self, name=None):
super().__init__()
self.name = name
self.debug_log = True
def debug_line(self, layer_str, output, memuse=1, final_call=False):
if self.debug_log:
namestr = '{}: '.format(self.name) if self.name is not None else ''
print('{}{:80s}: dims {}'.format(namestr, repr(layer_str),
output.shape))
if final_call:
self.debug_log = False
print()
def load_checkpoint(chkpt, model):
load_model_params(model, chkpt)
print(colorize("Checkpoint {} loaded!".format(chkpt), 'green'))
def load_model_params(model, path):
loaded_state = torch.load(path, map_location=lambda storage, loc: storage)
self_state = model.state_dict()
for name, param in loaded_state.items():
origname = name
if name not in self_state:
name = name.replace("module.", "")
if name not in self_state:
print(colorize("%s is not in the model." % origname, 'red'))
continue
if self_state[name].size() != param.size():
if np.prod(param.shape) == np.prod(self_state[name].shape):
print(
colorize(
"Caution! Parameter length: {}, model: {}, loaded: {}, Reshaping"
.format(origname, self_state[name].shape,
loaded_state[origname].shape), 'red'))
param = param.reshape(self_state[name].shape)
else:
print(
colorize(
"Wrong parameter length: {}, model: {}, loaded: {}".
format(origname, self_state[name].shape,
loaded_state[origname].shape), 'red'))
continue
self_state[name].copy_(param)
def calc_receptive_field(layers, imsize, layer_names=None):
if layer_names is not None:
print("-------Net summary------")
currentLayer = [imsize, 1, 1, 0.5]
for l_id, layer in enumerate(layers):
conv = [
layer[key][-1] if type(layer[key]) in [list, tuple] else layer[key]
for key in ['kernel_size', 'stride', 'padding']
]
currentLayer = outFromIn(conv, currentLayer)
if 'maxpool' in layer:
conv = [
(layer['maxpool'][key][-1] if type(layer['maxpool'][key])
in [list, tuple] else layer['maxpool'][key]) if
(not key == 'padding' or 'padding' in layer['maxpool']) else 0
for key in ['kernel_size', 'stride', 'padding']
]
currentLayer = outFromIn(conv, currentLayer, ceil_mode=False)
return currentLayer
def outFromIn(conv, layerIn, ceil_mode=True):
n_in = layerIn[0]
j_in = layerIn[1]
r_in = layerIn[2]
start_in = layerIn[3]
k = conv[0]
s = conv[1]
p = conv[2]
n_out = math.floor((n_in - k + 2 * p) / s) + 1
actualP = (n_out - 1) * s - n_in + k
pR = math.ceil(actualP / 2)
pL = math.floor(actualP / 2)
j_out = j_in * s
r_out = r_in + (k - 1) * j_in
start_out = start_in + ((k - 1) / 2 - pL) * j_in
return n_out, j_out, r_out, start_out
# ----------------------------------------------------------------------
def gpu_initializer(gpu_id):
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
global device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Using device: ', device)
return device
def my_unfold(tens, size, step, dimension, chunk_at_least):
"""
Unfolds into list allowing uneven last chunk, which is appending to the penultimate one
"""
fr = 0
out = []
done = 0
while fr < tens.shape[dimension]:
length = min(size, tens.shape[dimension] - fr)
if tens.shape[dimension] - (fr + length) < chunk_at_least:
# permit last chunk to be that longer so that it takes all the sequence
length = tens.shape[dimension] - fr
done = 1
out.append(tens.narrow(dimension, fr, length))
if done:
break
fr += step
return out
# ---------------------- peaks + NMS -------------------------------------
def detect_peaks(image, overlap_thresh=10):
"""
https://stackoverflow.com/questions/3684484/peak-detection-in-a-2d-array
Takes an image and detect the peaks usingthe local maximum filter.
Returns a boolean mask of the peaks (i.e. 1 when
the pixel's value is the neighborhood maximum, 0 otherwise)
"""
from scipy.ndimage.filters import maximum_filter
from scipy.ndimage.morphology import generate_binary_structure, binary_erosion
# define an 8-connected neighborhood
neighborhood = generate_binary_structure(2, 2)
# apply the local maximum filter; all pixel of maximal value
# in their neighborhood are set to 1
local_max = maximum_filter(image, footprint=neighborhood) == image
# local_max is a mask that contains the peaks we are
# looking for, but also the background.
# In order to isolate the peaks we must remove the background from the mask.
# we create the mask of the background
background = (image == 0)
# a little technicality: we must erode the background in order to
# successfully subtract it form local_max, otherwise a line will
# appear along the background border (artifact of the local maximum filter)
eroded_background = binary_erosion(background,
structure=neighborhood,
border_value=1)
# we obtain the final mask, containing only peaks,
# by removing the background from the local_max mask (xor operation)
detected_peaks = local_max ^ eroded_background
peak_coords = np.array(np.where(detected_peaks)).T
detected_peaks, peak_coords = non_max_suppression_fast(
detected_peaks, image, overlap_thresh=overlap_thresh)
return detected_peaks, peak_coords
# Malisiewicz et al.
def non_max_suppression_fast(peaks_map, values_map, overlap_thresh):
"""
adapted from
https://www.pyimagesearch.com/2015/02/16/faster-non-maximum-suppression-python/
"""
# if there are no boxes, return an empty list
if len(peaks_map) == 0:
return []
ww = peaks_map.shape[-1]
peak_coords = np.array(np.where(peaks_map)).T.astype(int)
values = values_map[np.where(peaks_map)]
# initialize the list of picked indexes
pick = []
idxs = np.argsort(values)
# keep looping while some indexes still remain in the indexes
# list
while len(idxs) > 0:
last = len(idxs) - 1
ii = idxs[last]
pick.append(ii)
dif = np.abs(peak_coords[ii] - peak_coords[idxs]).sum(
-1) # manhattan distance
idxs = np.delete(idxs, np.where(dif <= overlap_thresh))
peak_coords_out = peak_coords[pick].T
out_map = np.zeros_like(peaks_map)
out_map[(peak_coords_out[0], peak_coords_out[1])] = 1
debug = 0
if debug:
import matplotlib
matplotlib.use('GTK3Agg')
import matplotlib.pyplot as plt
plt.imshow(out_map)
plt.title('output binary')
plt.figure()
in_map = np.ones_like(values_map) * values_map.min()
in_map[(peak_coords.T[0],
peak_coords.T[1])] = values_map[(peak_coords.T[0],
peak_coords.T[1])]
plt.imshow(in_map)
plt.figure()
out_map_val = np.ones_like(values_map) * values_map.min()
out_map_val[(peak_coords_out[0],
peak_coords_out[1])] = values_map[(peak_coords_out[0],
peak_coords_out[1])]
plt.imshow(out_map_val)
plt.show()
return out_map, peak_coords_out.T
# ----------------- utils for mapping from original image coordinates (full) to feature map / attention map coordinates ----------------
def map_to_full_torch(map_in, w_frame, h_frame, offset):
offset, h_map, w_map, h_att, w_att = calc_map_offset(
offset, h_frame, w_frame, map_in.shape[-1])
import torch
interp = 'area'
map_full = torch.nn.functional.interpolate(map_in[None],
size=(h_map, w_map),
mode=interp).squeeze()
return map_full, offset
def map_to_full(map_in, w_frame, h_frame, offset, w_map=None):
w_map = w_map or map_in.shape[-1]
from PIL import Image
hm_im = Image.fromarray(map_in)
offset, h_map, w_map, h_att, w_att = calc_map_offset(
offset, h_frame, w_frame, w_map)
hm_im = hm_im.resize((w_map, h_map))
map_full = np.array(hm_im)
return map_full, offset
def calc_map_offset(offset, h_frame, w_frame, w_map):
# this is without the edge for going between map coords and original image pixels
w_att, h_att = w_frame - 2 * offset, h_frame - 2 * offset
edge = int(np.round((w_frame - 2 * offset) / (w_map - 1) / 2))
offset -= edge
w_map, h_map = w_frame - 2 * offset, h_frame - 2 * offset
return offset, h_map, w_map, h_att, w_att
def full_to_map(coords_full, h_full, w_full, h_map, w_map, start_offset):
# this is without the edge for going between map coords and original image pixels
w_att, h_att = w_full - 2 * start_offset, h_full - 2 * start_offset
map_ratio = np.array((h_att / h_map, w_att / w_map))
if isinstance(coords_full, torch.Tensor):
coords_map = torch.round((coords_full - start_offset) /
torch.from_numpy(map_ratio).float()).int()
coords_map[..., 0] = coords_map[..., 0].clamp(0, h_map - 1)
coords_map[..., 1] = coords_map[..., 1].clamp(0, w_map - 1)
assert (coords_map[...] < 0).sum() == 0
assert (coords_map[..., 0] > h_map).sum() == 0
assert (coords_map[..., 1] > w_map).sum() == 0
else:
coords_map = np.round(
(coords_full - start_offset) / map_ratio).astype(int)
coords_map[..., 0] = coords_map[..., 0].clip(0, h_map - 1)
coords_map[..., 1] = coords_map[..., 1].clip(0, w_map - 1)
return coords_map
# ----------------------------------------------------------------------------------------------------
# ---------------------- cropping utils ------------------------------------------------------------
def extract_attended_features(att_map, vid_emb, peak_traj_map):
weighted_feats = []
for b_id in range(len(vid_emb)):
# w_feats = torch.stack( [(fp * atw).sum(-1).sum(-1) for fp, atw in zip(feat_patches, att_weights)], 1)
width = 3
b_id_central_peak = peak_traj_map[b_id, :, slice(2, -2)]
feat_patches = torch.stack([
crop_feat_patch(vid_emb[b_id:b_id + 1], peak, width)
for peak in b_id_central_peak
], 1) # n_peaks x T x 1 x ff x h x w
att_weights = torch.stack([
crop_feat_patch(att_map[b_id:b_id + 1], peak, width, softmax=1)
for peak in b_id_central_peak
], 1) # n_peaks x T x 1 x 1 x h x w
w_feats = (feat_patches.to(att_weights.device) * att_weights).sum(
[-2, -1])
weighted_feats.append(w_feats)
w_feats = torch.cat(weighted_feats, 0)
# w_feats = w_feats.reshape((-1,) + w_feats.shape[2:])
return w_feats
def extract_face_crops(vid_frames_for_crop, avobject_traj, crop_size):
face_crop_vids = []
bs = len(vid_frames_for_crop)
for b_id in range(bs):
h_full, w_full = vid_frames_for_crop.shape[-2:]
face_crops = torch.stack([
crop_feat_patch(vid_frames_for_crop[b_id:b_id + 1],
peak.clip((0, 0), (h_full, w_full)), crop_size)
for peak in avobject_traj[b_id].astype(int)
], 1)
face_crop_vids.append(face_crops)
face_crop_vids = torch.cat(face_crop_vids, 0)
return face_crop_vids
def crop_feat_patch(feat_map, peak, ww, softmax=0):
# peak: T x 2
# feat_map: 1 x d x T x h x w
peak = peak.copy()
NEG_INF = -1e10
pad_val = NEG_INF if softmax else 0
padlen = ww // 2
feat_map = nn.ConstantPad2d([padlen, padlen, padlen, padlen],
pad_val)(feat_map)
peak += padlen
if len(peak.shape) == 1: # Same map over all time steps
c_y, c_x = peak
feat_patch = feat_map[...,
max(c_y - ww // 2, 0):c_y + ww // 2 + 1,
max(c_x - ww // 2, 0):c_x + ww // 2 + 1]
else:
feat_patch = []
# check that time dims are the same
assert peak.shape[0] == feat_map.shape[-3], 'Time dims are different'
for t_i, (c_y, c_x) in enumerate(peak):
fp = feat_map[..., t_i,
max(c_y - ww // 2, 0):c_y + ww // 2 + 1,
max(c_x - ww // 2, 0):c_x + ww // 2 + 1]
feat_patch.append(fp)
feat_patch = np.stack(feat_patch, 2) if isinstance(
feat_map, np.ndarray) else torch.stack(feat_patch, 2)
if softmax:
feat_patch = logsoftmax_2d(feat_patch).exp()
return feat_patch
# ----------------------
def logsoftmax_2d(logits):
# Log softmax on last 2 dims because torch won't allow multiple dims
orig_shape = logits.shape
logprobs = torch.nn.LogSoftmax(dim=-1)(
logits.reshape(list(logits.shape[:-2]) + [-1])).reshape(orig_shape)
return logprobs
def run_func_in_parts(func, vid_emb, aud_emb, part_len, dim, device):
"""
Run given function in parts, spliting the inputs on dimension dim
This is used to save memory when inputs too large to compute on gpu
"""
dist_chunk = []
for v_spl, a_spl in tqdm(list(
zip(vid_emb.split(part_len, dim=dim),
aud_emb.split(part_len, dim=dim))),
desc='Calculating pairwise scores'):
dist_chunk.append(func(v_spl.to(device), a_spl.to(device)))
dist = torch.cat(dist_chunk, dim - 1)
return dist
# ---------------------- flow wrapper -------------------------
def calc_flow_on_vid_wrapper(ims, tmp_dir='/dev/shm', gpu_id=0):
"""
Wrapper for calling PWC-net through a separate process
"""
# Free GPU memory before running flow in another process.
torch.cuda.empty_cache()
input_ims_path = tempfile.NamedTemporaryFile(suffix='.npy', dir=tmp_dir).name
output_flow_path = tempfile.NamedTemporaryFile(suffix='.npy', dir=tmp_dir).name
np.save(input_ims_path, ims)
command = "python flow/pwcnet.py {} {} {}".format(input_ims_path, output_flow_path, gpu_id)
from subprocess import call
cmd = command.split(' ')
call(cmd)
flow = np.load(output_flow_path)
os.remove(input_ims_path)
os.remove(output_flow_path)
return flow
# -------------------------- colorize utils -----------------------------------------------
"""
Borrowed from Tom Jakab & Ankush Gupta
https://github.com/tomasjakab/imm/blob/3f34424b853c9ead980a9b7f116d47b56d476b58/imm/utils/colorize.py
A set of common utilities used within the environments. These are
not intended as API functions, and will not remain stable over time.
"""
color2num = dict(gray=30,
red=31,
green=32,
yellow=33,
blue=34,
magenta=35,
cyan=36,
white=37,
crimson=38)
def colorize(string, color, bold=False, highlight=False):
"""Return string surrounded by appropriate terminal color codes to
print colorized text. Valid colors: gray, red, green, yellow,
blue, magenta, cyan, white, crimson
"""
# Import six here so that `utils` has no import-time dependencies.
# We want this since we use `utils` during our import-time sanity checks
# that verify that our dependencies (including six) are actually present.
import six
attr = []
num = color2num[color]
if highlight:
num += 10
attr.append(six.u(str(num)))
if bold:
attr.append(six.u('1'))
attrs = six.u(';').join(attr)
return six.u('\x1b[%sm%s\x1b[0m') % (attrs, string)
def green(s):
return colorize(s, 'green', bold=True)
def blue(s):
return colorize(s, 'blue', bold=True)
def red(s):
return colorize(s, 'red', bold=True)
def magenta(s):
return colorize(s, 'magenta', bold=True)