def verify_img_data(img_data, expected_output, mode):
if mode is None:
img = transforms.ToPILImage()(img_data)
assert img.mode == 'RGB' # default should assume RGB
else:
img = transforms.ToPILImage(mode=mode)(img_data)
assert img.mode == mode
split = img.split()
for i in range(3):
assert np.allclose(expected_output[i].numpy(), F.to_tensor(split[i]).numpy())
model_g = DSGAN.Generator(n_res_blocks=opt.num_res_blocks)
model_g.load_state_dict(torch.load(model_path), strict=True)
model_g.eval()
model_g = model_g.cuda()
print('# generator parameters:',
sum(param.numel() for param in model_g.parameters()))
# generate the noisy images
idx = 0
with torch.no_grad():
for file_hr, file_lr in zip(target_hr_files, target_lr_files):
idx += 1
print('Image No.:', idx)
# load HR image
input_img_hr = Image.open(file_hr)
input_img_hr = TF.to_tensor(input_img_hr)
# Save input_img as HR image for TDSR
path = os.path.join(tdsr_hr_dir, os.path.basename(file_hr))
TF.to_pil_image(input_img_hr).save(path, 'PNG')
# load LR image
input_img_lr = Image.open(file_lr)
input_img_lr = TF.to_tensor(input_img_lr)
# Apply model to generate the noisy resize_img
if torch.cuda.is_available():
input_img_lr = input_img_lr.unsqueeze(0).cuda()
resize_noisy_img = model_g(input_img_lr).squeeze(0).cpu()
def preprocess_image(image_path):
"Load Image, normalize and convert to tensor."
img = Image.open(image_path)
img_tensor = F.to_tensor(np.float32(img))
return fixed_image_standardization(image_tensor=img_tensor) # in [-1, 1]
def __call__(self, image, target):
return F.to_tensor(image), target
import argparse
from PIL import Image
from torchvision.transforms.functional import to_tensor
import torchjpeg.codec
parser = argparse.ArgumentParser("Tests the pytorch DCT loader by reading and image, quantizing its pixels, and writing the DCT coefficients to a JPEG")
parser.add_argument("input", help="Input image, should be lossless")
parser.add_argument("output", help="Output image, must be a JPEG")
parser.add_argument("quality", type=int, help="Output quality on the 0-100 scale")
args = parser.parse_args()
im = to_tensor(Image.open(args.input))
if im.shape[0] > 3:
im = im[:3]
dimensions, quantization, Y_coefficients, CbCr_coefficients = torchjpeg.codec.quantize_at_quality(im, args.quality)
torchjpeg.codec.write_coefficients(args.output, dimensions, quantization, Y_coefficients, CbCr_coefficients)
def __call__(self, results):
results['img'] = TF.to_tensor(results['img'].copy())
return results
def __call__(self, image, mask, joints, area):
return F.to_tensor(image), mask, joints, area
cur_land[1] = proj_num_rows - 1 - cur_land[1]
# Indicators that there is enough of a femur visible in the field of view
# that we can use ground truth pose for any additional experiments, such as
# evaluating another femur registration method
left_femur_good_fov = cur_proj_g[
'gt-poses/left-femur-good-fov'][()]
right_femur_good_fov = cur_proj_g[
'gt-poses/right-femur-good-fov'][()]
# Next the segmentation labels and landmark locations will be overlaid on the projections
pil = TF.to_pil_image(cur_proj)
pil = pil.convert('RGB')
cur_proj = TF.to_tensor(pil)
pil = None
# alpha blending for segmentation overlay of pixels that are not background
# 0 --> seg. not visible, only projection shows
# 1 --> only seg. shows, proj. not visible in seg. regions
alpha = 0.35
label_colors = [
[0.0, 1.0, 0.0], # green
[1.0, 0.0, 0.0], # red
[0.0, 0.0, 1.0], # blue
[1.0, 1.0, 0.0], # yellow
[0.0, 1.0, 1.0], # cyan
[1.0, 0.5, 0.0]
] # orange
def test_image(img):
global model
global params
model.eval()
# img = Image.open(img_path).convert('RGB')
img = cv2.resize(img, dsize=(1280, 720))
img_pil = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_pil = Image.fromarray(img)
# w, h = img.size
h, w, _ = img.shape
print(img.shape)
img_pil = F.crop(img_pil, h-640, 0, 640, w)
img_pil = F.resize(img_pil, size=(256, 512), interpolation=Image.BILINEAR)
input = F.to_tensor(img_pil).float()
# print("input:")
# print(type(input))
# print("input size:")
# print(input.size())
print("##################")
input = torch.Tensor([torch.Tensor.numpy(input)])
# print("input:")
# print(type(input))
# print("input size:")
# print(input.size())
# Reset coordinates
x_cal0, x_cal1, x_cal2, x_cal3 = [None]*4
# Put inputs on gpu if possible
if not args.no_cuda:
input = input.cuda(non_blocking=True).float()
# Run model
torch.cuda.synchronize()
a = time.time()
start1 = time.time()
beta0, beta1, beta2, beta3, weightmap_zeros, \
output_net, outputs_line, outputs_horizon, output_seg = model(input, gt_line=np.array([1,1]),
end_to_end=args.end_to_end, gt=None)
torch.cuda.synchronize()
b = time.time()
# Horizon task & Line classification task
if args.clas:
horizon_pred = nn.Sigmoid()(outputs_horizon).sum(dim=1)
horizon_pred = (torch.round((resize_coordinates(horizon_pred) + 80)/10)*10).int()
line_pred = torch.round(nn.Sigmoid()(outputs_line))
else:
assert False
# Calculate X coordinates
x_cal0 = params.compute_coordinates(beta0)
x_cal1 = params.compute_coordinates(beta1)
x_cal2 = params.compute_coordinates(beta2)
x_cal3 = params.compute_coordinates(beta3)
lanes_pred = torch.stack((x_cal0, x_cal1, x_cal2, x_cal3), dim=1)
print("DL FPS: {0}".format(1.0/(time.time()-start1)))
# Check line type branch
line_pred = line_pred[:, [1, 2, 0, 3]]
lanes_pred[(1 - line_pred[:, :, None]).byte().expand_as(lanes_pred)] = -2
# Check horizon branch
bounds = ((horizon_pred - 160) / 10)
for k, bound in enumerate(bounds):
lanes_pred[k, :, :bound.item()] = -2
# TODO check intersections
lanes_pred[lanes_pred > 1279] = -2
lanes_pred[lanes_pred < 0] = -2
lanes_pred = np.int_(np.round(lanes_pred.data.cpu().numpy())).tolist()
num_el = input.size(0)
for j in range(num_el):
lanes_to_write = lanes_pred[j]
if args.draw_testset:
test = weightmap_zeros[j]
weight0= test[0]
weight1= test[1]
weight2= test[2]
weight3= test[3]
# img_name = img_path
h_samples = [160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670, 680, 690, 700, 710]
colormap = [(255,0,0), (0,255,0), (255,255,0), (0,0,255), (0, 128, 128)]
# with open(img_name, 'rb') as f:
# img = np.array(Image.open(f).convert('RGB'))
for lane_i in range(len(lanes_to_write)):
x_orig = lanes_to_write[lane_i]
pt_or = [(xcord, ycord) for (xcord, ycord) in zip(x_orig, h_samples) if xcord!=-2]
for point in pt_or:
img = cv2.circle(img, tuple(np.int32(point)), thickness=-1, color=colormap[lane_i], radius = 3)
# img = Image.fromarray(np.uint8(img))
# img.show()
return img
def read_im():
im = Image.open('data/night.jpg')
im = TF.crop(im, 16, 0, 704, 1280)
im = TF.to_tensor(im)
im = im - 0.5
return im[None, ...]
def __call__(self, sample):
image, image_seg, label = sample['image'], sample['image_seg'], sample[
'label']
img = trF.to_tensor(image)
img_seg = trF.to_tensor(image_seg)
return {'image': img, 'image_seg': img_seg, 'label': label}
def __call__(self, data):
data['input'] = F.to_tensor(data['input']).float()
data['label'] = F.to_tensor(data['label']).float()
return data
def __call__(self, image, *args):
return (F.to_tensor(image), ) + args
def get_contour_gain_vs_length(model,
device_to_use,
g_params,
k_idx,
ch_mus,
ch_sigmas,
rslt_dir,
c_len_arr,
frag_size=np.array([7, 7]),
full_tile_size=np.array([14, 14]),
img_size=np.array([256, 256, 3]),
n_images=50,
epsilon=1e-5,
iou_results=True):
"""
:param iou_results:
:param c_len_arr:
:param rslt_dir:
:param epsilon:
:param model:
:param device_to_use:
:param g_params:
:param k_idx:
:param ch_mus:
:param ch_sigmas:
:param frag_size:
:param full_tile_size:
:param img_size:
:param n_images:
:return:
"""
global edge_extract_act
global cont_int_in_act
global cont_int_out_act
# tracking variables -------------------------------------------------
iou_arr = []
tgt_n = k_idx
max_act_n_idx = g_params[0]['extra_info']['max_active_neuron_idx']
tgt_n_out_acts = np.zeros((n_images, len(c_len_arr)))
max_act_n_acts = np.zeros_like(tgt_n_out_acts)
# -----------------------------------------------------------------
frag = gabor_fits.get_gabor_fragment(g_params, spatial_size=frag_size)
bg = g_params[0]['bg']
for c_len_idx, c_len in enumerate(c_len_arr):
print("Processing contour length = {}".format(c_len))
iou = 0
for img_idx in range(n_images):
# (1) Create Test Image
test_img, test_img_label, contour_frags_starts, end_acc_angle, start_acc_angle = \
fields1993_stimuli.generate_contour_image(
frag=frag,
frag_params=g_params,
c_len=c_len,
beta=0,
alpha=0,
f_tile_size=full_tile_size,
img_size=img_size,
random_alpha_rot=True,
rand_inter_frag_direction_change=True,
use_d_jitter=False,
bg_frag_relocate=True,
bg=bg
)
test_img = transform_functional.to_tensor(test_img)
test_img_label = torch.from_numpy(
np.array(test_img_label)).unsqueeze(0)
# # Debug - Plot Test Image
# # ------------------------
# if img_idx == 0:
# disp_img = np.transpose(test_img.numpy(), axes=(1, 2, 0))
# disp_img = (disp_img - disp_img.min()) / (disp_img.max() - disp_img.min()) * 255.
# disp_img = disp_img.astype('uint8')
# disp_label = test_img_label.numpy()
#
# print(disp_label)
# print("Label is valid? {}".format(fields1993_stimuli.is_label_valid(disp_label)))
#
# plt.figure()
# plt.imshow(disp_img)
# plt.title("Input Image. Contour Length = {}".format(c_len))
#
# # Highlight Label Tiles
# disp_label_image = fields1993_stimuli.plot_label_on_image(
# disp_img,
# disp_label,
# full_tile_size,
# edge_color=(250, 0, 0),
# edge_width=2,
# display_figure=False
# )
#
# # Highlight All background Tiles
# full_tile_starts = fields1993_stimuli.get_background_tiles_locations(
# frag_len=full_tile_size[0],
# img_len=img_size[1],
# row_offset=0,
# space_bw_tiles=0,
# tgt_n_visual_rf_start=img_size[0] // 2 - (full_tile_size[0] // 2)
# )
#
# disp_label_image = fields1993_stimuli.highlight_tiles(
# disp_label_image,
# full_tile_size,
# full_tile_starts,
# edge_color=(255, 255, 0))
#
# plt.figure()
# plt.imshow(disp_label_image)
# plt.title("Labeled Image. Countour Length = {}".format(c_len))
# (2) Get output Activations
if iou_results:
label = test_img_label
iou += process_image(model, device_to_use, ch_mus, ch_sigmas,
test_img, label)
else:
label = None
process_image(model, device_to_use, ch_mus, ch_sigmas,
test_img, label)
center_n_acts = \
cont_int_out_act[
0, :, cont_int_out_act.shape[2] // 2, cont_int_out_act.shape[3] // 2]
tgt_n_out_acts[img_idx, c_len_idx] = center_n_acts[tgt_n]
max_act_n_acts[img_idx, c_len_idx] = center_n_acts[max_act_n_idx]
iou_arr.append(iou / n_images)
# # ---------------------------------
# import pdb
# pdb.set_trace()
# plt.close('all')
# IOU
if iou_results:
# print("IoU per length {}".format(iou_arr))
f_title = "Iou vs length - Neuron {}".format(k_idx)
f_name = "neuron {}".format(k_idx)
plot_iou_vs_contour_length(c_len_arr, iou_arr, rslt_dir, f_title,
f_name)
# -------------------------------------------
# Gain
# -------------------------------------------
# In Li2006, Gain was defined as output of neuron / mean output to noise pattern
# where the noise pattern was defined as optimal stimulus at center of RF and all
# others fragments were random. This corresponds to resp c_len=x/ mean resp clen=1
tgt_n_avg_noise_resp = np.mean(tgt_n_out_acts[:, 0])
max_active_n_avg_noise_resp = np.mean(max_act_n_acts[:, 0])
tgt_n_gains = tgt_n_out_acts / (tgt_n_avg_noise_resp + epsilon)
max_active_n_gains = max_act_n_acts / (max_active_n_avg_noise_resp +
epsilon)
tgt_n_mean_gain_arr = np.mean(tgt_n_gains, axis=0)
tgt_n_std_gain_arr = np.std(tgt_n_gains, axis=0)
max_act_n_mean_gain_arr = np.mean(max_active_n_gains, axis=0)
max_act_n_std_gain_arr = np.std(max_active_n_gains, axis=0)
# -----------------------------------------------------------------------------------
# Plots
# -----------------------------------------------------------------------------------
# Gain vs Length
# f = plt.figure()
f, ax_arr = plt.subplots(1, 2)
ax_arr[0].errorbar(c_len_arr,
tgt_n_mean_gain_arr,
tgt_n_std_gain_arr,
label='Target Neuron {}'.format(tgt_n))
ax_arr[1].errorbar(c_len_arr,
max_act_n_mean_gain_arr,
max_act_n_std_gain_arr,
label='Max Active Neuron {}'.format(max_act_n_idx))
ax_arr[0].set_xlabel("Contour Length")
ax_arr[1].set_xlabel("Contour Length")
ax_arr[0].set_ylabel("Gain")
ax_arr[1].set_ylabel("Gain")
ax_arr[0].set_ylim(bottom=0)
ax_arr[1].set_ylim(bottom=0)
ax_arr[0].grid()
ax_arr[1].grid()
ax_arr[0].legend()
ax_arr[1].legend()
f.suptitle("Contour Gain Vs length - Neuron {}".format(k_idx))
f.savefig(os.path.join(rslt_dir, 'gain_vs_len.jpg'), format='jpg')
plt.close(f)
# Output activations vs Length
f = plt.figure()
plt.errorbar(c_len_arr,
np.mean(tgt_n_out_acts, axis=0),
np.std(tgt_n_out_acts, axis=0),
label='target_neuron_{}'.format(tgt_n))
plt.errorbar(c_len_arr,
np.mean(max_act_n_acts, axis=0),
np.std(max_act_n_acts, axis=0),
label='max_active_neuron_{}'.format(max_act_n_idx))
plt.legend()
plt.grid()
plt.xlabel("Contour Length")
plt.ylabel("Activations")
plt.title("Output Activations")
f.savefig(os.path.join(rslt_dir, 'output_activations_vs_len.jpg'),
format='jpg')
plt.close(f)
# Save output Activations
tgt_n_mean_out_acts = np.mean(tgt_n_out_acts, axis=0)
tgt_n_std_out_acts = np.std(tgt_n_out_acts, axis=0)
return iou_arr, tgt_n_mean_gain_arr, tgt_n_std_gain_arr, max_act_n_mean_gain_arr, \
max_act_n_std_gain_arr, tgt_n_avg_noise_resp, max_active_n_avg_noise_resp, \
tgt_n_mean_out_acts, tgt_n_std_out_acts
def find_optimal_stimulus(model,
device_to_use,
k_idx,
ch_mus,
ch_sigmas,
extract_point,
frag_size=np.array([7, 7]),
img_size=np.array([256, 256, 3])):
"""
:return:
"""
global edge_extract_act
global cont_int_in_act
global cont_int_out_act
orient_arr = np.arange(0, 180, 5)
img_center = img_size[0:2] // 2
tgt_n_acts = np.zeros((len(base_gabor_parameters), len(orient_arr)))
tgt_n_max_act = 0
tgt_n_opt_params = None
for base_gp_idx, base_gabor_params in enumerate(base_gabor_parameters):
print("Processing Base Gabor Param Set {}".format(base_gp_idx))
for o_idx, orient in enumerate(orient_arr):
# Change orientation
g_params = copy.deepcopy(base_gabor_params)
for c_idx in range(len(g_params)):
g_params[c_idx]["theta_deg"] = orient
# Create Test Image - Single fragment @ center
frag = gabor_fits.get_gabor_fragment(g_params,
spatial_size=frag_size)
bg = base_gabor_params[0]['bg']
if bg is None:
bg = fields1993_stimuli.get_mean_pixel_value_at_boundary(frag)
test_img = np.ones(img_size, dtype='uint8') * bg
add_one = 1
if frag_size[0] % 2 == 0:
add_one = 0
test_img[img_center[0] - frag_size[0] // 2:img_center[0] +
frag_size[0] // 2 + add_one,
img_center[0] - frag_size[0] // 2:img_center[0] +
frag_size[0] // 2 + add_one, :, ] = frag
test_img = transform_functional.to_tensor(test_img)
# # Debug - Show Test Image
# plt.figure()
# plt.imshow(np.transpose(test_img,axes=(1, 2, 0)))
# plt.title("Input Image - Find optimal stimulus")
# import pdb
# pdb.set_trace()
# Get target activations
process_image(model, device_to_use, ch_mus, ch_sigmas, test_img)
if extract_point == 'edge_extract_layer_out':
center_n_acts = \
edge_extract_act[
0, :, edge_extract_act.shape[2]//2, edge_extract_act.shape[3]//2]
elif extract_point == 'contour_integration_layer_in':
center_n_acts = \
cont_int_in_act[
0, :, cont_int_in_act.shape[2]//2, cont_int_in_act.shape[3]//2]
else: # 'contour_integration_layer_out'
center_n_acts = \
cont_int_out_act[
0, :, cont_int_out_act.shape[2]//2, cont_int_out_act.shape[3]//2]
tgt_n_act = center_n_acts[k_idx]
tgt_n_acts[base_gp_idx, o_idx] = tgt_n_act
# # # Debug - Display all channel responses to individual test image
# plt.figure()
# plt.plot(center_n_acts)
# plt.title("Center Neuron Activations. Base Gabor Set {}. Orientation {}".format(
# base_gp_idx, orient))
if tgt_n_act > tgt_n_max_act:
tgt_n_max_act = tgt_n_act
tgt_n_opt_params = copy.deepcopy(g_params)
max_active_n = int(np.argmax(center_n_acts))
extra_info = {
'optim_stim_act_value': tgt_n_max_act,
'optim_stim_base_gabor_set': base_gp_idx,
'optim_stim_act_orient': orient,
'max_active_neuron_is_target': (max_active_n == k_idx),
'max_active_neuron_value': center_n_acts[max_active_n],
'max_active_neuron_idx': max_active_n
}
for item in tgt_n_opt_params:
item['extra_info'] = extra_info
# # -----------------------------------------
# # Debug - Tuning Curve for Individual base Gabor Params
# plt.figure()
# plt.plot(orient_arr, tgt_n_acts[base_gp_idx, :])
# plt.title("Neuron {}: responses vs Orientation. Gabor Set {}".format(k_idx, base_gp_idx))
# import pdb
# pdb.set_trace()
# ---------------------------
if tgt_n_opt_params is not None:
# Save optimal tuning curve
for item in tgt_n_opt_params:
opt_base_g_params_set = item['extra_info'][
'optim_stim_base_gabor_set']
item['extra_info']['orient_tuning_curve_x'] = orient_arr
item['extra_info']['orient_tuning_curve_y'] = tgt_n_acts[
opt_base_g_params_set, ]
# # Debug: plot tuning curves for all gabor sets
# # ------------------------------------------------
# plt.figure()
# for base_gp_idx, base_gabor_params in enumerate(base_gabor_parameters):
#
# if base_gp_idx == tgt_n_opt_params[0]['extra_info']['optim_stim_base_gabor_set']:
# line_width = 5
# plt.plot(
# tgt_n_opt_params[0]['extra_info']['optim_stim_act_orient'],
# tgt_n_opt_params[0]['extra_info']['max_active_neuron_value'],
# marker='x', markersize=10,
# label='max active neuron Index {}'.format(
# tgt_n_opt_params[0]['extra_info']['max_active_neuron_idx'])
# )
# else:
# line_width = 2
#
# plt.plot(
# orient_arr, tgt_n_acts[base_gp_idx, ],
# label='param set {}'.format(base_gp_idx), linewidth=line_width
# )
#
# plt.legend()
# plt.grid(True)
# plt.title(
# "Kernel {}. Max Active Base Set {}. Is most responsive to this stimulus {}".format(
# k_idx,
# tgt_n_opt_params[0]['extra_info']['optim_stim_base_gabor_set'],
# tgt_n_opt_params[0]['extra_info']['max_active_neuron_is_target'])
# )
#
# import pdb
# pdb.set_trace()
return tgt_n_opt_params
def to_tensor(self, img):
img = Image.fromarray(img)
img_t = F.to_tensor(img).float()
return img_t
def __call__(self, image, target):
image = F.to_tensor(image).contiguous()
return image, target
def load(cfg):
all_imgs, all_poses = [], []
counts = [0]
for s in ['train', 'val', 'test']:
meta = None
fname = os.path.join(cfg.dataset.path, 'transforms_{}.json'.format(s))
with open(fname, 'r') as fp:
meta = json.load(fp)
imgs = []
poses = []
if s == 'train' or cfg.dataset.testskip < 2:
skip = 1
else:
skip = cfg.dataset.testskip
for frame in meta['frames'][::skip]:
fname = os.path.join(cfg.dataset.path, frame['file_path'] + '.png')
im = Image.open(fname)
if cfg.dataset.half_res:
im = TF.resize(im, (400, 400))
imgs.append(TF.to_tensor(im))
poses.append(torch.Tensor(frame['transform_matrix']))
# keep all 4 channels (RGBA)
imgs = torch.stack(imgs, dim=0).permute(0, 2, 3, 1)
poses = torch.stack(poses, dim=0)
counts.append(counts[-1] + imgs.shape[0])
all_imgs.append(imgs)
all_poses.append(poses)
i_split = [torch.arange(counts[i], counts[i + 1]) for i in range(3)]
imgs = torch.cat(all_imgs, dim=0)
poses = torch.cat(all_poses, dim=0)
H, W = imgs.shape[1:3]
camera_angle_x = float(meta['camera_angle_x'])
focal = .5 * W / torch.tan(torch.Tensor([.5 * camera_angle_x]))
hwf = [int(H), int(W), focal.numpy()[0]]
render_poses = torch.stack([
pose_spherical(angle, -30., 4.)
for angle in torch.linspace(-180., 180., 41)[:-1]
], dim=0)
if cfg.train.white_background:
imgs = imgs[..., :3] * imgs[..., -1:] + (1. - imgs[..., -1:])
else:
imgs = imgs[..., :3]
print(
'Loaded blender',
cfg.dataset.path,
imgs.shape,
poses.shape,
render_poses.shape,
hwf
)
return imgs, poses, render_poses, i_split, hwf
def data_transform(img, im_size):
img = img.resize(im_size, Image.BILINEAR)
img = F.to_tensor(img) # convert to tensor (values between 0 and 1)
img = F.normalize(img, MEAN, STD) # normalize the tensor
return img
def __call__(self, img, target):
return F.to_tensor(img), target
def __call__(self, images, intrinsics):
tensors = [F.to_tensor(im) for im in images]
return tensors, torch.from_numpy(intrinsics)
def __call__(self, sample):
return {'original_image': F.to_tensor(sample['original_image']),
'downsampled_image': F.to_tensor(sample['downsampled_image']),
'label': sample['label']}
def transform_image(self, image):
#img_resized_tensor = TF.to_tensor(image)
normalize_img = (image - 127.5) / 127.5
return TF.to_tensor(normalize_img).type(torch.FloatTensor)
def to_tensor(im1, im2):
a, b = im1
a = tvF.to_tensor(a)
b = tvF.to_tensor(b)
im2 = tvF.to_tensor(im2)
return torch.cat([a, b]), im2
def resize_batch(self, label, size):
"""Resize and convert to tensor"""
resized = F.resize(label, size, interpolation=Image.NEAREST)
return F.to_tensor(resized) * 255
def main():
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
parser = argparse.ArgumentParser(description='Test trained models')
parser.add_argument(
'--options-file',
'-o',
default='options-and-config.pickle',
type=str,
help='The file where the simulation options are stored.')
parser.add_argument('--checkpoint-file',
'-c',
required=True,
type=str,
help='Model checkpoint file')
parser.add_argument('--batch-size',
'-b',
default=12,
type=int,
help='The batch size.')
parser.add_argument('--source-image',
'-s',
required=True,
type=str,
help='The image to watermark')
# parser.add_argument('--times', '-t', default=10, type=int,
# help='Number iterations (insert watermark->extract).')
args = parser.parse_args()
train_options, hidden_config, noise_config = utils.load_options(
args.options_file)
noiser = Noiser(noise_config)
checkpoint = torch.load(args.checkpoint_file)
hidden_net = Hidden(hidden_config, device, noiser, None)
utils.model_from_checkpoint(hidden_net, checkpoint)
image_pil = Image.open(args.source_image)
image = randomCrop(np.array(image_pil), hidden_config.H, hidden_config.W)
image_tensor = TF.to_tensor(image).to(device)
image_tensor = image_tensor * 2 - 1 # transform from [0, 1] to [-1, 1]
image_tensor.unsqueeze_(0)
# for t in range(args.times):
message = torch.Tensor(
np.random.choice(
[0, 1],
(image_tensor.shape[0], hidden_config.message_length))).to(device)
losses, (encoded_images, noised_images,
decoded_messages) = hidden_net.validate_on_batch(
[image_tensor, message])
decoded_rounded = decoded_messages.detach().cpu().numpy().round().clip(
0, 1)
message_detached = message.detach().cpu().numpy()
print('original: {}'.format(message_detached))
print('decoded : {}'.format(decoded_rounded))
print('error : {:.3f}'.format(
np.mean(np.abs(decoded_rounded - message_detached))))
utils.save_images(image_tensor.cpu(),
encoded_images.cpu(),
'test',
'.',
resize_to=(256, 256))
def compute_rcnn_embs(ds_root,
mode='train',
debug=False,
world_size=1,
rank=0):
# type: (str, str, bool, int, int) -> None
"""
:param ds_root: Cityflow-NL dataset root (absolute path)
:return:
"""
print(f"Started process {rank}")
# Load BERT model
gpu_id = rank % torch.cuda.device_count()
model = fasterrcnn_resnet50_fpn(pretrained=True).to(f"cuda:{gpu_id}")
model.eval()
userful_classes = [1, 2, 3, 4, 5, 7, 8, 10, 11, 12, 13, 14]
# Load train json
if mode == 'train':
tracks_root = os.path.join(ds_root, 'data/train-tracks.json')
elif mode == 'test':
tracks_root = os.path.join(ds_root, 'data/test-tracks.json')
else:
raise Exception(f"Only train and test are valid split/modes")
with open(tracks_root, "r") as f:
tracks = json.load(f)
keys = list(tracks.keys())
# Output
out_dir = os.path.join(ds_root, f"rcnn_embs_{mode}")
if os.path.isdir(out_dir):
# Remove already computed keys
prec_keys = [k.split('.')[0] for k in os.listdir(out_dir)]
keys = [k for k in keys if k not in prec_keys]
for key_idx, id in tqdm(enumerate(keys), total=len(keys)):
if (key_idx % world_size) != rank:
continue
result = {
"frames": [],
"detected_boxes": [],
"features": [],
"ego_ind": []
}
frames = tracks[id]['frames']
ego = tracks[id]['boxes']
for frame_path, ego in zip(frames, ego):
frame_abspath = os.path.join(ds_root, frame_path)
frame_orig = Image.open(frame_abspath)
frame = to_tensor(frame_orig).to(f"cuda:{gpu_id}")
with torch.no_grad():
# object detection
predictions = model([
frame,
])
boxes = predictions[0]["boxes"].cpu()
features = predictions[0]["features"].cpu()
labels = predictions[0]["labels"].cpu()
scores = predictions[0]["scores"].cpu()
# Filter boxes based on class and confidence score
labels_filter = [
i for i, l in enumerate(labels) if l in userful_classes
]
scores_filter = [i for i, s in enumerate(scores) if s > 0.65]
indices = [
i for i in range(len(boxes))
if i in labels_filter and i in scores_filter
]
# determine index of the ego vehicle (if detected)
ego_bb = np.array(ego)[np.newaxis, :]
# (x,y,w,h) -> (x1,y1,x2,y2)
ego_bb[:,
2], ego_bb[:,
3] = ego_bb[:,
2] + ego_bb[:,
0], ego_bb[:,
3] + ego_bb[:,
1]
if len(labels) > 1: # at least one box is detected
ious = iou_of(boxes.numpy(), ego_bb)
ego_ind = np.argmax(ious)
if ious[ego_ind] < 0.2:
ego_ind = -1
else:
if ego_ind not in indices:
indices.append(ego_ind.item())
ego_ind = len(indices) - 1 # the last element
else:
ego_ind = indices.index(
ego_ind) # position relative to indices not labels
else:
ego_ind = -1
# prepare result
boxes[:,
2], boxes[:,
3] = boxes[:,
2] - boxes[:,
0], boxes[:,
3] - boxes[:,
1] # (x1,y1,x2,y2) -> (x,y,w,h)
boxes = torch.cat([labels[:, None], boxes, scores[:, None]],
dim=-1) # class, (x,y,w,h), score
if len(indices) == 0:
print(1)
# filter
boxes = boxes[indices]
features = features[indices]
result["frames"].append(frame_path)
result["detected_boxes"].append(boxes)
result["features"].append(features)
result["ego_ind"].append(ego_ind)
# save
out_file = os.path.join(out_dir, f'{id}.pt')
torch.save(result, out_file)
def evaluate(cls, env, model, r_idx, resnet, traj_data, args, lock,
successes, failures, results):
# reset model
model.reset()
# setup scene
reward_type = 'dense'
cls.setup_scene(env, traj_data, r_idx, args, reward_type=reward_type)
# extract language features
feat = model.featurize([(traj_data, False)], load_mask=False)
# goal instr
goal_instr = traj_data['turk_annotations']['anns'][r_idx]['task_desc']
maskrcnn = maskrcnn_resnet50_fpn(num_classes=119)
maskrcnn.eval()
maskrcnn.load_state_dict(torch.load('weight_maskrcnn.pt'))
maskrcnn = maskrcnn.cuda()
prev_image = None
prev_action = None
nav_actions = [
'MoveAhead_25', 'RotateLeft_90', 'RotateRight_90', 'LookDown_15',
'LookUp_15'
]
prev_class = 0
prev_center = torch.zeros(2)
done, success = False, False
fails = 0
t = 0
reward = 0
while not done:
# break if max_steps reached
if t >= args.max_steps:
break
# extract visual features
curr_image = Image.fromarray(np.uint8(env.last_event.frame))
feat['frames'] = resnet.featurize([curr_image],
batch=1).unsqueeze(0)
# forward model
m_out = model.step(feat)
m_pred = model.extract_preds(m_out, [(traj_data, False)],
feat,
clean_special_tokens=False)
m_pred = list(m_pred.values())[0]
# action prediction
action = m_pred['action_low']
if prev_image == curr_image and prev_action == action and prev_action in nav_actions and action in nav_actions and action == 'MoveAhead_25':
dist_action = m_out['out_action_low'][0][0].detach().cpu()
idx_rotateR = model.vocab['action_low'].word2index(
'RotateRight_90')
idx_rotateL = model.vocab['action_low'].word2index(
'RotateLeft_90')
action = 'RotateLeft_90' if dist_action[
idx_rotateL] > dist_action[
idx_rotateR] else 'RotateRight_90'
if action == cls.STOP_TOKEN:
print("\tpredicted STOP")
break
# mask prediction
mask = None
if model.has_interaction(action):
class_dist = m_pred['action_low_mask'][0]
pred_class = np.argmax(class_dist)
# mask generation
with torch.no_grad():
out = maskrcnn([to_tensor(curr_image).cuda()])[0]
for k in out:
out[k] = out[k].detach().cpu()
if sum(out['labels'] == pred_class) == 0:
mask = np.zeros(
(constants.SCREEN_WIDTH, constants.SCREEN_HEIGHT))
else:
masks = out['masks'][out['labels'] ==
pred_class].detach().cpu()
scores = out['scores'][out['labels'] ==
pred_class].detach().cpu()
# Instance selection based on the minimum distance between the prev. and cur. instance of a same class.
if prev_class != pred_class:
scores, indices = scores.sort(descending=True)
masks = masks[indices]
prev_class = pred_class
prev_center = masks[0].squeeze(
dim=0).nonzero().double().mean(dim=0)
else:
cur_centers = torch.stack([
m.nonzero().double().mean(dim=0)
for m in masks.squeeze(dim=1)
])
distances = ((cur_centers - prev_center)**2).sum(dim=1)
distances, indices = distances.sort()
masks = masks[indices]
prev_center = cur_centers[0]
mask = np.squeeze(masks[0].numpy(), axis=0)
# print action
if args.debug:
print(action)
# use predicted action and mask (if available) to interact with the env
t_success, _, _, err, _ = env.va_interact(
action,
interact_mask=mask,
smooth_nav=args.smooth_nav,
debug=args.debug)
if not t_success:
fails += 1
if fails >= args.max_fails:
print("Interact API failed %d times" % fails +
"; latest error '%s'" % err)
break
# next time-step
t_reward, t_done = env.get_transition_reward()
reward += t_reward
t += 1
prev_image = curr_image
prev_action = action
# check if goal was satisfied
goal_satisfied = env.get_goal_satisfied()
if goal_satisfied:
print("Goal Reached")
success = True
# goal_conditions
pcs = env.get_goal_conditions_met()
goal_condition_success_rate = pcs[0] / float(pcs[1])
# SPL
path_len_weight = len(traj_data['plan']['low_actions'])
s_spl = (1 if goal_satisfied else 0) * min(
1., path_len_weight / (float(t) + 1e-4))
pc_spl = goal_condition_success_rate * min(
1., path_len_weight / (float(t) + 1e-4))
# path length weighted SPL
plw_s_spl = s_spl * path_len_weight
plw_pc_spl = pc_spl * path_len_weight
# log success/fails
lock.acquire()
log_entry = {
'trial': traj_data['task_id'],
'type': traj_data['task_type'],
'repeat_idx': int(r_idx),
'goal_instr': goal_instr,
'completed_goal_conditions': int(pcs[0]),
'total_goal_conditions': int(pcs[1]),
'goal_condition_success': float(goal_condition_success_rate),
'success_spl': float(s_spl),
'path_len_weighted_success_spl': float(plw_s_spl),
'goal_condition_spl': float(pc_spl),
'path_len_weighted_goal_condition_spl': float(plw_pc_spl),
'path_len_weight': int(path_len_weight),
'reward': float(reward)
}
if success:
successes.append(log_entry)
else:
failures.append(log_entry)
# overall results
results['all'] = cls.get_metrics(successes, failures)
print("-------------")
print("SR: %d/%d = %.5f" % (results['all']['success']['num_successes'],
results['all']['success']['num_evals'],
results['all']['success']['success_rate']))
print("PLW SR: %.5f" %
(results['all']['path_length_weighted_success_rate']))
print(
"GC: %d/%d = %.5f" %
(results['all']['goal_condition_success']
['completed_goal_conditions'],
results['all']['goal_condition_success']['total_goal_conditions'],
results['all']['goal_condition_success']
['goal_condition_success_rate']))
print(
"PLW GC: %.5f" %
(results['all']['path_length_weighted_goal_condition_success_rate']
))
print("-------------")
# task type specific results
task_types = [
'pick_and_place_simple', 'pick_clean_then_place_in_recep',
'pick_heat_then_place_in_recep', 'pick_cool_then_place_in_recep',
'pick_two_obj_and_place', 'look_at_obj_in_light',
'pick_and_place_with_movable_recep'
]
for task_type in task_types:
task_successes = [
s for s in (list(successes)) if s['type'] == task_type
]
task_failures = [
f for f in (list(failures)) if f['type'] == task_type
]
if len(task_successes) > 0 or len(task_failures) > 0:
results[task_type] = cls.get_metrics(task_successes,
task_failures)
else:
results[task_type] = {}
lock.release()
def __call__(self, image, target):
image = F.to_tensor(image)
return image, target
def act(self, image, player_info):
"""
:param image: numpy array of shape (300, 400, 3)
:param player_info: pystk.Player object for the current kart.
return: Dict describing the action
"""
global FIRST, IM, BACKUP
score_goal = None
if self.team == 0:
score_goal = GOAL_0
else:
score_goal = GOAL_1
front = np.array(HACK_DICT['kart'].front)[[0, 2]]
kart = np.array(HACK_DICT['kart'].location)[[0, 2]]
puck = np.array(HACK_DICT['state'].soccer.ball.location)[[0, 2]]
# player vector
u = front - kart
u = u / np.linalg.norm(u)
# player to puck
v = puck - kart
v = v / np.linalg.norm(v)
# goal to puck
w = puck - score_goal
w = w / np.linalg.norm(w)
# adjust for scoring
v2 = v + (w / 2)
v2 = v2 / np.linalg.norm(v2)
theta = np.arccos(np.dot(u, v2))
signed_theta = -np.sign(np.cross(u, v2)) * theta
steer = 5 * signed_theta
accel = random.uniform(0.4, 0.8)
brake = False
drift = False
if np.degrees(theta) > 20 and np.degrees(theta) < 90:
drift = True
if np.degrees(theta) > 90 and not BACKUP:
BACKUP = True
if BACKUP:
if np.degrees(theta) > 30:
accel = 0
brake = True
steer = -steer
else:
BACKUP = False
p_info = []
mask = (HACK_DICT['race'].render_data[self.player_id].instance ==
134217729)
mask = F.to_tensor(mask)
nz = torch.nonzero(mask)
if nz.numel() == 0:
HACK_DICT['player_bool_%d' % self.player_id] = False
else:
HACK_DICT['player_bool_%d' % self.player_id] = True
p_info.extend(kart)
p_info.extend(u)
peak = self.extract_peak(nz)
p_info.extend(peak)
#test = self.get_puck_coords(image)
#p_info.extend(self.get_puck_coords(image))
HACK_DICT['player_info_%d' % self.player_id] = p_info
HACK_DICT['puck_vec_%d' % self.player_id] = (puck - kart)
# visualize the controller in real time
# if player_info.kart.id == 0:
# ax1 = plt.subplot(111)
# if FIRST:
# IM = ax1.imshow(image)
# FIRST = False
# else:
# IM.set_data(image)
# # print('p_to_fron: ', u)
# # print('WORLD p_to_puck: ', (puck - kart))
# # puck_xy = self.get_puck_coords(image)
# # screen_p_to_puck = np.array([200, 180]) - puck_xy
# # screen_p_to_puck[0] = -screen_p_to_puck[0]
# # print('SCREEN p_to_puck: ', screen_p_to_puck)
# # print('puck_to_g: ', w)
# # print('aim_vec__: ', v2)
# # print('signed theta: ', signed_theta)
# # print('steer: ', steer)
# # print('loc: ' + str(kart))
# # print('-----------------------')
# plt.pause(0.001)
action = {
'steer': steer,
'acceleration': accel,
'brake': brake,
'drift': drift,
'nitro': False,
'rescue': False
}
return action
height = scene_size[1]
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
traj_old = traj.clone().numpy()
traj[:, 0] = traj[:, 0] - width // 2
traj[:, 1] = traj[:, 1] - height // 2
traj = traj.unsqueeze(0)
traj = traj.permute(0, 2, 1)
traj = traj.to(device)
rel_traj = absolute_to_rel_traj(traj).to(device)
logger.info("Moving static image to the device")
image = Image.open(path_of_static_image)
scene = TF.to_tensor(image)
scene.unsqueeze_(0)
scene = scene.to(device)
# Create model.
desire = DESIRE(IOCParams(),
SGMParams())
logger.info("Created model")
logger.debug(desire)
logger.info("Moving to device: {}".format(device))
desire.to(device)
logger.info("Loading state dict")
state_dict_checkpoint = torch.load(restore_model_path)
desire.load_state_dict(state_dict_checkpoint)
state_dict_checkpoint = torch.load(restore_model_path)
