def binary_search_helper(dimension, logger, model_name="EDSR", device="cuda"):
"""
Process random image and calculates processing time
Parameters
----------
dimension : int
random image dimension.
logger : logger
keep logs.
device : str, optional
GPU or CPU. The default is 'cuda'.
Returns
-------
total_time : float
EDSR processing time.
"""
print('Before loading model: ')
subprocess.run("gpustat", shell=True)
print()
total_time = 0
try:
model = None
if model_name == "EDSR":
model = md.load_edsr(device=device)
print('After loading model: ')
subprocess.run("gpustat", shell=True)
print()
elif model_name == "RRDB":
model = md.load_rrdb(device=device)
else:
raise Exception("Unknown model...")
model.eval()
input_image = ut.random_image(dimension)
if model_name == "RRDB":
input_image = input_image[:, 2:, :, :]
input_image = input_image.to(device)
with torch.no_grad():
start = time.time()
print('Before processing: ')
subprocess.run("gpustat", shell=True)
output_image = model(input_image)
print('After processing: ')
subprocess.run("gpustat", shell=True)
end = time.time()
total_time = end - start
ut.clear_cuda(input_image, output_image)
model.cpu()
del model
print('After model shifting and deleting: ')
subprocess.run("gpustat", shell=True)
except RuntimeError as err:
logger.error("Runtime error for dimension: {}x{}: " + err)
sys.exit(1)
return total_time
def trt_forward_chop_iterative(
x,
trt_engine_path=None,
shave=10,
min_size=1024,
device="cuda",
print_result=True,
scale=4,
use_fp16=False,
):
"""
Forward chopping in an iterative way
Parameters
----------
x : tensor
input image.
model : nn.Module, optional
SR model. The default is None.
shave : int, optional
patch shave value. The default is 10.
min_size : int, optional
total patch size (dimension x dimension) . The default is 1024.
device : int, optional
GPU or CPU. The default is 'cuda'.
print_result : bool, optional
print result or not. The default is True.
Returns
-------
output : tensor
output image.
total_time : float
total execution time.
total_crop_time : float
total cropping time.
total_shift_time : float
total GPU to CPU shfiting time.
total_clear_time : float
total GPU clearing time.
"""
dim = int(math.sqrt(min_size)) # getting patch dimension
b, c, h, w = x.size() # current image batch, channel, height, width
device = device
patch_count = 0
output = torch.tensor(np.zeros((b, c, h * 4, w * 4))).numpy()
total_time = 0
total_crop_time = 0
total_shift_time = 0
total_clear_time = 0
extra = x.clone().detach()
f = open(trt_engine_path, "rb")
runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING))
engine = runtime.deserialize_cuda_engine(f.read())
context = engine.create_execution_context()
new_i_s = 0
stream = cuda.Stream()
for i in range(0, h, dim - 2 * shave):
new_j_s = 0
new_j_e = 0
for j in range(0, w, dim - 2 * shave):
patch_count += 1
h_s, h_e = i, min(h, i + dim) # patch height start and end
w_s, w_e = j, min(w, j + dim) # patch width start and end
lr = x[:, :, h_s:h_e, w_s:w_e]
ba, ch, ht, wt = lr.shape
print('\nx: {}\n'.format(x))
print('\nlr: {}\n'.format(lr))
input_lr = torch.tensor(lr).int()
output_folder = "output_images"
file_name = "data/test7.jpg".split("/")[-1].split(".")[0]
ut.save_image(input_lr[0].int(),
output_folder,
ht,
wt,
4,
output_file_name=file_name + f"input_{i}_{j}_x4")
lr = lr.numpy()
print(f"shape of lr:{lr.shape}")
# EDSR processing
start = time.time()
# torch.cuda.synchronize()
USE_FP16 = use_fp16
target_dtype = np.float16 if USE_FP16 else np.float32
ba, ch, ht, wt = lr.shape
lr = np.ascontiguousarray(lr, dtype=np.float32)
# need to set input and output precisions to FP16 to fully enable it
p_output = np.empty([b, c, ht * scale, wt * scale],
dtype=target_dtype)
# allocate device memory
d_input = cuda.mem_alloc(1 * lr.nbytes)
d_output = cuda.mem_alloc(1 * p_output.nbytes)
bindings = [int(d_input), int(d_output)]
sr = predict(context, lr, d_input, stream, bindings, p_output,
d_output)
output_sr = torch.tensor(sr).int()
output_folder = "output_images"
file_name = "data/test7.jpg".split("/")[-1].split(".")[0]
ut.save_image(output_sr[0],
output_folder,
ht,
wt,
4,
output_file_name=file_name + f"{i}_{j}_x4")
# torch.cuda.synchronize()
end = time.time()
processing_time = end - start
total_time += processing_time
# new cropped patch's dimension (h and w)
n_h_s, n_h_e, n_w_s, n_w_e = 0, 0, 0, 0
n_h_s = 0 if h_s == 0 else (shave * 4)
n_h_e = ((h_e - h_s) * 4) if h_e == h else (((h_e - h_s) - shave) *
4)
new_i_e = new_i_s + n_h_e - n_h_s
n_w_s = 0 if w_s == 0 else (shave * 4)
n_w_e = ((w_e - w_s) * 4) if w_e == w else (((w_e - w_s) - shave) *
4)
new_j_e = new_j_e + n_w_e - n_w_s
# corpping image in
crop_start = time.time()
sr_small = sr[:, :, n_h_s:n_h_e, n_w_s:n_w_e]
crop_end = time.time()
crop_time = crop_end - crop_start
total_crop_time += crop_time
output[:, :, new_i_s:new_i_e, new_j_s:new_j_e] = sr_small
del sr_small
clear_start = time.time()
if device == "cuda":
ut.clear_cuda(None, None)
clear_end = time.time()
clear_time = clear_end - clear_start
total_clear_time += clear_time
if w_e == w:
break
new_j_s = new_j_e
new_i_s = new_i_e
if h_e == h:
break
return output, total_time, total_crop_time, total_shift_time, total_clear_time
def trt_forward_chop_iterative_v2(
x,
trt_engine_path=None,
shave=10,
min_size=1024,
device="cuda",
print_result=True,
scale=4,
use_fp16=False,
):
"""
Parameters
----------
x : 4d array
input image.
trt_engine_path : str, optional
path of the trt engine. The default is None.
shave : int, optional
shave value. The default is 10.
min_size : int, optional
total size of the image. The default is 1024.
device : str, optional
device cuda or cpu. The default is "cuda".
print_result : bool, optional
print result or not. The default is True.
scale : int, optional
hr = scale * lr. The default is 4.
use_fp16 : bool, optional
choose precision. The default is False.
Raises
------
Exception
DESCRIPTION.
Returns
-------
output : TYPE
DESCRIPTION.
"""
patch_count = 0
row_count = 0
column_count = 0
dim = int(math.sqrt(min_size)) # getting patch dimension
b, c, img_height, img_width = x.size(
) # current image batch, channel, height, width
device = device
output = torch.tensor(np.zeros(
(b, c, img_height * 4, img_width * 4))).numpy()
f = open(trt_engine_path, "rb")
runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING))
engine = runtime.deserialize_cuda_engine(f.read())
context = engine.create_execution_context()
new_i_s = 0
new_i_s = 0 # new patch height start
for patch_height_start in range(0, img_height, dim - 2 * shave):
row_count += 1
right_most = False
bottom_most = False
left_increased = 0
top_increased = 0
new_j_s = 0
new_j_e = 0
for patch_width_start in range(0, img_width, dim - 2 * shave):
column_count += 1
patch_count += 1
patch_height_end = min(img_height, patch_height_start + dim)
patch_width_end = min(img_width, patch_width_start + dim)
if img_height < patch_height_start + dim:
bottom_most = True
old_patch_height_start = patch_height_start
patch_height_start = img_height - dim
patch_height_start = ((img_height - dim) if
(img_height - dim) >= 0 else 0)
top_increased = old_patch_height_start - patch_height_start
if img_width < patch_width_start + dim:
right_most = True
old_patch_width_start = patch_width_start
patch_width_start = img_width - dim
patch_width_start = (img_width -
dim) if (img_width - dim) >= 0 else 0
left_increased = old_patch_width_start - patch_width_start
left_crop, top_crop, right_crop, bottom_crop = (
0,
0,
shave * scale,
shave * scale,
)
if patch_width_start != 0:
if right_most == True:
left_crop = (shave + left_increased) * scale
else:
left_crop = shave * scale
if patch_height_start != 0:
if bottom_most == True:
top_crop = (shave + top_increased) * scale
else:
top_crop = shave * scale
if patch_width_end == img_width:
right_crop = 0
if patch_height_end == img_height:
bottom_crop = 0
# =============================================================================
# print('Patch no: {}, Row: {}, Column: {}\n'.format(patch_count, row_count, column_count))
# print('{}x{}:{}x{}'.format(patch_height_start, patch_height_end, patch_width_start, patch_width_end ))
# print('SR Patch size: {}x{}'.format(dim*scale, dim*scale))
# =============================================================================
h_s, h_e, w_s, w_e = (
0 + top_crop,
dim * scale - bottom_crop,
0 + left_crop,
dim * scale - right_crop,
)
# =============================================================================
# print('hs, he, ws, we', h_s, h_e, w_s, w_e)
# =============================================================================
if dim >= img_height and dim >= img_width:
h_s, h_e, w_s, w_e = 0, img_height * scale, 0, img_width * scale
elif dim < img_height and dim >= img_width:
w_s, w_e = 0, img_width * scale
elif dim >= img_height and dim < img_width:
h_s, h_e = 0, img_height * scale
lr = x[:, :, patch_height_start:patch_height_end,
patch_width_start:patch_width_end]
# =============================================================================
# print('x.shape: ',x.shape)
# print('lr.shape', lr.shape)
# =============================================================================
ba, ch, ht, wt = lr.shape
lr = lr.numpy()
# EDSR processing
start = time.time()
# torch.cuda.synchronize()
USE_FP16 = use_fp16
target_dtype = np.float16 if USE_FP16 else np.float32
ba, ch, ht, wt = lr.shape
lr = np.ascontiguousarray(lr, dtype=target_dtype)
# need to set input and output precisions to FP16 to fully enable it
p_output = np.empty([b, c, ht * scale, wt * scale],
dtype=target_dtype)
# allocate device memory
#subprocess.run("gpustat", shell=True)
d_input = cuda.mem_alloc(1 * lr.nbytes)
d_output = cuda.mem_alloc(1 * p_output.nbytes)
#subprocess.run("gpustat", shell=True)
bindings = [int(d_input), int(d_output)]
stream = cuda.Stream()
sr = predict(context, lr, d_input, stream, bindings, p_output,
d_output)
#subprocess.run("gpustat", shell=True)
new_i_e = new_i_s + h_e - h_s
new_j_e = new_j_s + w_e - w_s
patch_crop_positions = [h_s, h_e, w_s, w_e]
SR_positions = [new_i_s, new_i_e, new_j_s, new_j_e]
# torch.cuda.synchronize()
end = time.time()
processing_time = end - start
sr_small = sr[:, :, h_s:h_e, w_s:w_e]
output[:, :, new_i_s:new_i_e, new_j_s:new_j_e] = sr_small
del sr_small
#subprocess.run("gpustat", shell=True)
clear_start = time.time()
if device == "cuda":
ut.clear_cuda(None, None)
new_j_s = new_j_e
if patch_width_end == img_width:
break
new_i_s = new_i_e
column_count = 0
if patch_height_end == img_height:
break
if patch_count == 0:
raise Exception("Shave size too big for given patch dimension")
#subprocess.run("gpustat", shell=True)
return output
def forward_chop_iterative(x,
model=None,
shave=10,
min_size=1024,
device="cuda",
print_result=True):
"""
Forward chopping in an iterative way
Parameters
----------
x : tensor
input image.
model : nn.Module, optional
SR model. The default is None.
shave : int, optional
patch shave value. The default is 10.
min_size : int, optional
total patch size (dimension x dimension) . The default is 1024.
device : int, optional
GPU or CPU. The default is 'cuda'.
print_result : bool, optional
print result or not. The default is True.
Returns
-------
output : tensor
output image.
total_time : float
total execution time.
total_crop_time : float
total cropping time.
total_shift_time : float
total GPU to CPU shfiting time.
total_clear_time : float
total GPU clearing time.
"""
dim = int(math.sqrt(min_size)) # getting patch dimension
b, c, h, w = x.size() # current image batch, channel, height, width
device = device
patch_count = 0
output = torch.tensor(np.zeros((b, c, h * 4, w * 4)))
total_time = 0
total_crop_time = 0
total_shift_time = 0
total_clear_time = 0
# =============================================================================
# if device == "cuda":
# torch.cuda.synchronize()
# x = x.to(device)
# =============================================================================
new_i_s = 0
for i in range(0, h, dim - 2 * shave):
new_j_s = 0
new_j_e = 0
for j in range(0, w, dim - 2 * shave):
patch_count += 1
h_s, h_e = i, min(h, i + dim) # patch height start and end
w_s, w_e = j, min(w, j + dim) # patch width start and end
# subprocess.run("gpustat", shell=True)
# =============================================================================
# print(
# "Patch no: {} : {}-{}x{}-{}\n".format(patch_count, h_s, h_e, w_s, w_e)
# )
# =============================================================================
lr = x[:, :, h_s:h_e, w_s:w_e]
# =============================================================================
# print(lr.shape)
# =============================================================================
if device == "cuda":
torch.cuda.synchronize()
lr = lr.to(device)
with torch.no_grad():
# EDSR processing
start = time.time()
torch.cuda.synchronize()
sr = model(lr)
torch.cuda.synchronize()
end = time.time()
processing_time = end - start
total_time += processing_time
# =============================================================================
# print('Processing time: ', processing_time)
# =============================================================================
shift_start = time.time()
torch.cuda.synchronize()
sr = sr.cpu()
torch.cuda.synchronize()
shift_end = time.time()
shift_time = shift_end - shift_start
# new cropped patch's dimension (h and w)
n_h_s, n_h_e, n_w_s, n_w_e = 0, 0, 0, 0
n_h_s = 0 if h_s == 0 else (shave * 4)
n_h_e = ((h_e - h_s) * 4) if h_e == h else (((h_e - h_s) - shave) *
4)
new_i_e = new_i_s + n_h_e - n_h_s
n_w_s = 0 if w_s == 0 else (shave * 4)
n_w_e = ((w_e - w_s) * 4) if w_e == w else (((w_e - w_s) - shave) *
4)
new_j_e = new_j_e + n_w_e - n_w_s
# corpping image in
crop_start = time.time()
sr_small = sr[:, :, n_h_s:n_h_e, n_w_s:n_w_e]
crop_end = time.time()
crop_time = crop_end - crop_start
total_crop_time += crop_time
# =============================================================================
# shift_start = time.time()
# if device == "cuda":
# torch.cuda.synchronize()
# sr_small = sr_small.to("cpu")
# torch.cuda.synchronize()
# shift_end = time.time()
# shift_time = shift_end - shift_start
# =============================================================================
total_shift_time += shift_time
output[:, :, new_i_s:new_i_e, new_j_s:new_j_e] = sr_small
del sr_small
clear_start = time.time()
if device == "cuda":
ut.clear_cuda(lr, sr)
clear_end = time.time()
clear_time = clear_end - clear_start
total_clear_time += clear_time
if w_e == w:
break
new_j_s = new_j_e
new_i_s = new_i_e
if h_e == h:
break
# =============================================================================
# if print_result == True:
# print("Patch dimension: {}x{}".format(dim, dim))
# print("Total pacthes: ", patch_count)
# print("Total EDSR Processing time: ", total_time)
# print("Total crop time: ", total_crop_time)
# print("Total shift time: ", total_shift_time)
# print("Total clear time: ", total_clear_time)
# =============================================================================
return output, total_time, total_crop_time, total_shift_time, total_clear_time
def forward_chop_iterative(x, model=None, shave=10, min_size=1024):
dim = round(math.sqrt(min_size))
b, c, h, w = x.size()
device = "cuda"
count = 0
output = torch.tensor(np.zeros((b, c, h * 4, w * 4)))
total_time = 0
new_i_s = 0
x = x.to(device)
for i in tqdm(range(0, h, dim - 2 * shave)):
new_j_s = 0
new_j_e = 0
# =============================================================================
# subprocess.run("gpustat", shell=True)
# =============================================================================
for j in range(0, w, dim - 2 * shave):
# =============================================================================
# print(i,j)
# subprocess.run("gpustat", shell=True)
# =============================================================================
count += 1
h_s = i
h_e = min(h, i + dim)
w_s = j
w_e = min(w, j + dim)
lr = x[:, :, h_s:h_e, w_s:w_e]
# =============================================================================
# print('h: {}x{} w: {}x{}'.format(h_s, h_e, w_s, w_e))
# print('current dim: {}x{}'.format(h_e-h_s,w_e-w_s))
# =============================================================================
with torch.no_grad():
# lr = lr.to(device)
# =============================================================================
# subprocess.run("gpustat", shell=True)
# =============================================================================
start = time.time()
sr = model(lr)
end = time.time()
processing_time = end - start
total_time += processing_time
# =============================================================================
# subprocess.run("gpustat", shell=True)
# =============================================================================
# sr = sr.detach().numpy()
n_h = (h_e - h_s) * 4
n_w = (w_e - w_s) * 4
# =============================================================================
# print('next_dimension: {}x{}'.format(n_h, n_w))
# =============================================================================
n_h_s, n_h_e, n_w_s, n_w_e = 0, 0, 0, 0
if h_s == 0:
n_h_s = 0
else:
n_h_s = shave * 4
if h_e == h:
n_h_e = (h_e - h_s) * 4
else:
n_h_e = ((h_e - h_s) - shave) * 4
new_i_e = new_i_s + n_h_e - n_h_s
if w_s == 0:
n_w_s = 0
else:
n_w_s = shave * 4
if w_e == w:
n_w_e = (w_e - w_s) * 4
else:
n_w_e = ((w_e - w_s) - shave) * 4
new_j_e = new_j_e + n_w_e - n_w_s
sr_small = sr[:, :, n_h_s:n_h_e, n_w_s:n_w_e]
sr_small = sr_small.to("cpu")
output[:, :, new_i_s:new_i_e, new_j_s:new_j_e] = sr_small
del sr_small
# =============================================================================
# print('new -> h: {}x{} w: {}x{}'.format(n_h_s, n_h_e, n_w_s, n_w_e))
# print('h-> {}:{}, w-> {}:{}'.format(new_i_s, new_i_e, new_j_s, new_j_e))
# print()
# =============================================================================
if w_e == w:
break
new_j_s = new_j_e
ut.clear_cuda(lr, sr)
# =============================================================================
# subprocess.run("gpustat", shell=True)
# print('-----------------------------------------------------')
# =============================================================================
# =============================================================================
# print('After:')
# subprocess.run("gpustat", shell=True)
# print()
# =============================================================================
new_i_s = new_i_e
if h_e == h:
break
# =============================================================================
# print(count)
# print(output.shape)
# =============================================================================
print("Patch dimension: {}x{}".format(dim, dim))
print("Total pacthes: ", count)
print("Total EDSR Processing time: ", total_time)
return output
def batch_forward_chop(
patch_list,
batch_size,
channel,
img_height,
img_width,
dim,
shave,
scale,
model,
device="cuda",
print_timer=True,
):
"""
Create SR image from batches of patches
Parameters
----------
patch_list : list
list of patches.
batch_size : int
batch size.
channel : int
input image channel.
img_height : int
input image height.
img_width : int
input image width.
dim : int
patch dimension.
shave : int
shave value for patch.
scale : int
scale for LR to SR.
model : nn.Module
SR model.
device : str, optional
GPU or CPU. The default is 'cuda'.
print_timer : bool, optional
Print result or not. The default is True.
Raises
------
Exception
DESCRIPTION.
Returns
-------
3D matrix, tuple
output_image, tuple of timings.
"""
logger = ut.get_logger()
total_patches = len(patch_list)
if batch_size > total_patches:
sys.exit(2)
raise Exception("Batch size greater than total number of patches")
output_image = torch.tensor(
np.zeros((channel, img_height * scale, img_width * scale)))
cpu_to_gpu_time = 0
gpu_to_cpu_time = 0
batch_creating_time = 0
total_EDSR_time = 0
cuda_clear_time = 0
merging_time = 0
for start in range(1, total_patches + 1, batch_size):
info = ""
try:
batch_creating_timer = ut.timer()
batch = []
end = start + batch_size
if start + batch_size > total_patches:
end = total_patches + 1
for p in range(start, end):
batch.append(patch_list[p][4])
batch_creating_time += batch_creating_timer.toc()
torch.cuda.synchronize()
cpu_to_gpu_timer = ut.timer()
batch = torch.stack(batch).to(device)
torch.cuda.synchronize()
cpu_to_gpu_time += cpu_to_gpu_timer.toc()
info = (info + "C2G Starts: " + str(cpu_to_gpu_timer.t0) +
"C2G total: " + str(cpu_to_gpu_time))
# =============================================================================
# print(batch.shape)
# subprocess.run("gpustat", shell=True)
# =============================================================================
with torch.no_grad():
# =============================================================================
# print(start, end)
# print(sys.getsizeof(batch))
# =============================================================================
torch.cuda.synchronize()
start_time = time.time()
sr_batch = model(batch)
torch.cuda.synchronize()
end_time = time.time()
processing_time = end_time - start_time
total_EDSR_time += processing_time
info = (info + "\tModel Starts: " + str(start_time) +
"Model total: " + str(total_EDSR_time))
torch.cuda.synchronize()
gpu_to_cpu_timer = ut.timer()
sr_batch = sr_batch.to("cpu")
torch.cuda.synchronize()
gpu_to_cpu_time += gpu_to_cpu_timer.toc()
info = (info + "\tGPU 2 CPU Starts: " + str(gpu_to_cpu_timer.t0) +
"G2C total: " + str(gpu_to_cpu_time))
_, _, patch_height, patch_width = sr_batch.size()
logger.info(info)
batch_id = 0
merging_timer = ut.timer()
for p in range(start, end):
output_image[:, patch_list[p][3][0]:patch_list[p][3][
1], patch_list[p][3][2]:patch_list[p][3][3], ] = sr_batch[
batch_id][:, patch_list[p][2][0]:patch_list[p][2][1],
patch_list[p][2][2]:patch_list[p][2][3], ]
batch_id += 1
merging_time += merging_timer.toc()
cuda_clear_timer = ut.timer()
ut.clear_cuda(batch, None)
cuda_clear_time += cuda_clear_timer.toc()
except RuntimeError as err:
ut.clear_cuda(batch, None)
raise Exception(err)
model = model.to("cpu")
if print_timer:
print("Total upsampling time: {}\n".format(total_EDSR_time))
print("Total CPU to GPU shifting time: {}\n".format(cpu_to_gpu_time))
print("Total GPU to CPU shifting time: {}\n".format(gpu_to_cpu_time))
print("Total batch creation time: {}\n".format(batch_creating_time))
print("Total merging time: {}\n".format(merging_time))
print("Total CUDA clear time: {}\n".format(cuda_clear_time))
print("Total time: {}\n".format(total_EDSR_time + cpu_to_gpu_time +
gpu_to_cpu_time + batch_creating_time +
cuda_clear_time + merging_time))
return output_image, (
total_EDSR_time,
cpu_to_gpu_time,
gpu_to_cpu_time,
batch_creating_time,
cuda_clear_time,
merging_time,
)
def maximum_acceptable_dimension(device,
logger,
model,
max_unacceptable_dimension,
model_name="EDSR"):
"""
Get amximum acceptable dimension
Parameters
----------
device : str
device type.
model : torch.nn.model
SR model.
max_unacceptable_dimension : int
Maximum unacceptable dimension which is apower of 2.
Returns
-------
last : int
acceptable dimension.
"""
print("\nGetting maximum acceptable dimension...\n")
result2 = {}
dimension = max_unacceptable_dimension
maxm = math.inf
minm = -math.inf
last = 0
last_used_memory = 0
iteration = 0
while True:
# Printing iterations status
iteration += 1
_, used_memory, _ = ut.get_gpu_details(device,
None,
logger,
print_details=False)
leaked_memory = (used_memory - last_used_memory
if used_memory > last_used_memory else 0)
print(
"Patch Dimension: {:04}x{:04} | Used Memory: {:09.3f} | Leaked Memory: {:09.3f} | Iteration: {}"
.format(dimension, dimension, used_memory, leaked_memory,
iteration))
last_used_memory = used_memory
# Clearing cuda cache:
ut.clear_cuda(None, None)
# Binary Search
if last == dimension:
break
process_output = subprocess.run(
["python3", "binarysearch_helper.py",
str(dimension), model_name],
stdout=subprocess.PIPE,
text=True,
)
if process_output.returncode == 0:
out = process_output.stdout.split("\n")
total_time = out[0]
last = dimension
if dimension in result2.keys():
result2[dimension].append(total_time)
else:
result2[dimension] = [total_time]
minm = copy.copy(dimension)
if maxm == math.inf:
dimension *= 2
else:
dimension = dimension + (maxm - minm) // 2
ut.clear_cuda(None, None)
else:
ut.get_gpu_details(
device,
"Runtime error for dimension: {}x{}".format(
dimension, dimension),
logger,
)
maxm = copy.copy(dimension)
if dimension in result2.keys():
result2[dimension].append(math.inf)
else:
result2[dimension] = [math.inf]
if minm == -math.inf:
dimension = dimension // 2
else:
dimension = minm + (maxm - minm) // 2
ut.clear_cuda(None, None)
return last
def build_onnx_trt(model_name, patch_dim, use_precision, verbose):
if patch_dim == None:
config = toml.load("../config.toml")
patch_dim = int(config["max_dim"])
else:
patch_dim = int(patch_dim)
# pytorch to onnx model
if verbose:
print("Building ONNX model from the PyTorch model...")
onnx_model_name = model_name.lower() + "_" + str(use_precision)+ "_" + \
str(patch_dim) + ".onnx"
# =============================================================================
# omb.build_onnx_model(model_name, patch_dim, onnx_model_name)
# =============================================================================
# =============================================================================
# command1 = "python3 onnx_model_builder.py " + str(model_name) + " " + \
# str(patch_dim) + " " + str(onnx_model_name)
# =============================================================================
command1 = [
"python3", "onnx_model_builder.py",
str(model_name),
str(patch_dim),
str(onnx_model_name)
]
#subprocess.run(command1, shell=True)
while True:
process_output = subprocess.run(
command1,
stdout=subprocess.PIPE,
text=True,
)
if process_output.returncode != 0:
ut.clear_cuda(None, None)
patch_dim -= 1
print('Memory out. Decreasing patch size. New patch_size = {}'.
format(patch_dim))
onnx_model_name = model_name.lower() + "_" + str(use_precision)+ "_" + \
str(patch_dim) + ".onnx"
command1 = [
"python3", "onnx_model_builder.py",
str(model_name),
str(patch_dim),
str(onnx_model_name)
]
else:
ut.clear_cuda(None, None)
# for linear search
config = toml.load("../config.toml")
config["max_dim"] = patch_dim
f = open("../config.toml", "w")
toml.dump(config, f)
break
# onnx to trt
if verbose:
print("Building TRT engine from the ONNX model...")
trt_model = "inference_models/" + model_name.lower() + "_" + str(use_precision) + \
"_" + str(patch_dim) + ".trt"
if use_precision == "fp32":
command2 = "python3 onnx_trt_util.py " + "inference_models/"+onnx_model_name + " " + \
str(trt_model) + " 0"
elif use_precision == "fp16":
command2 = "python3 onnx_trt_util.py " + "inference_models/"+onnx_model_name + " " + \
str(trt_model) + " 1"
subprocess.run(command2, shell=True)
def maximum_unacceptable_dimension_2n(device,
logger,
start_dim=2,
model_name="EDSR"):
"""
Ge the maximum unacceptable dimension which is apower of 2
Parameters
----------
device : str
device type.
model : torch.nn.model
SR model.
Returns
-------
last_dimension : int
unacceptabel dimension.
"""
print(
"\nGetting maximum unacceptable dimension which is a power of two...\n"
)
result1 = {}
last_dimension = 0
dimension = start_dim
last_used_memory = 0
iteration = 0
while True:
# Prinitng loop status
iteration += 1
_, used_memory, _ = ut.get_gpu_details(device,
None,
logger,
print_details=False)
leaked_memory = (used_memory - last_used_memory
if used_memory > last_used_memory else 0)
print(
"Patch Dimension: {:04}x{:04} | Used Memory: {:09.3f} | Leaked Memory: {:09.3f} | Iteration: {}"
.format(dimension, dimension, used_memory, leaked_memory,
iteration))
last_used_memory = used_memory
# Calling SR model for different dimension
process_output = subprocess.run(
["python3", "binarysearch_helper.py",
str(dimension), model_name],
stdout=subprocess.PIPE,
text=True,
)
if process_output.returncode == 0:
out = process_output.stdout.split("\n")
total_time = out[0]
if dimension in result1.keys():
result1[dimension].append(total_time)
else:
result1[dimension] = [total_time]
dimension *= 2
else:
ut.get_gpu_details(
device,
"Runtime error for dimension: {}x{}".format(
dimension, dimension),
logger,
)
if dimension in result1.keys():
result1[dimension].append(math.inf)
else:
result1[dimension] = [math.inf]
last_dimension = dimension
ut.clear_cuda(None, None)
break
return last_dimension
def do_binary_search(model_name, start_dim):
"""
Binary search function...
Returns
-------
None.
"""
# Prints the header banner
banner = pyfiglet.figlet_format("Binary Search: " + model_name)
print(banner)
# Getting logger
logger = ut.get_logger()
# Check valid model or not
if model_name not in ["EDSR", "RRDB"]:
logger.exception("{} model is unkknown".format(model_name))
raise Exception("Unknown model...")
# Device type cpu or cuda
device = ut.get_device_type()
if device == "cpu" and model_name not in ["EDSR"]:
logger.exception("{} model cannot be run in CPU".format(model_name))
raise Exception("{} model cannot be run in CPU".format(model_name))
# Device information
_, device_name = ut.get_device_details()
if device == "cuda":
logger.info("Device: {}, Device Name: {}".format(device, device_name))
ut.get_gpu_details(
device,
"Before binary search: {}".format(model_name),
logger,
print_details=True,
)
else:
logger.info("Device: {}, Device Name: {}".format(device, device_name))
# Clearing cuda cache
ut.clear_cuda(None, None)
# Getting the highest unacceptable dimension which is a power of 2
max_unacceptable_dimension = maximum_unacceptable_dimension_2n(
device, logger, start_dim=start_dim, model_name=model_name)
print("\nMaximum unacceptable dimension: {}\n".format(
max_unacceptable_dimension))
# Clearing cuda cache
ut.clear_cuda(None, None)
# Getting the maximum acceptable dimension
max_dim = maximum_acceptable_dimension(device,
logger,
None,
max_unacceptable_dimension,
model_name=model_name)
print("\nMaximum acceptable dimension: {}\n".format(max_dim))
# Clearing cuda cache
ut.clear_cuda(None, None)
# For batch processing
config = toml.load("../batch_processing.toml")
config["end_patch_dimension"] = max_dim
f = open("../batch_processing.toml", "w")
toml.dump(config, f)
# for linear search
config = toml.load("../config.toml")
config["max_dim"] = max_dim
f = open("../config.toml", "w")
toml.dump(config, f)
def do_linear_search(test=False, test_dim=32):
"""
Linear search function...
Returns
-------
None.
"""
logger = ut.get_logger()
device = "cuda"
model_name = "EDSR"
config = toml.load("../config.toml")
run = config["run"]
scale = int(config["scale"]) if config["scale"] else 4
# device information
_, device_name = ut.get_device_details()
total, _, _ = ut.get_gpu_details(
device, "\nDevice info:", logger, print_details=False
)
log_message = (
"\nDevice: "
+ device
+ "\tDevice name: "
+ device_name
+ "\tTotal memory: "
+ str(total)
)
logger.info(log_message)
ut.clear_cuda(None, None)
state = "Before loading model: "
total, used, _ = ut.get_gpu_details(device, state, logger, print_details=True)
model = md.load_edsr(device=device)
state = "After loading model: "
total, used, _ = ut.get_gpu_details(device, state, logger, print_details=True)
# =============================================================================
# file = open("temp_max_dim.txt", "r")
# line = file.read()
# max_dim = int(line.split(":")[1])
# =============================================================================
config = toml.load("../config.toml")
max_dim = int(config["max_dim"])
if test == False:
detailed_result, memory_used, memory_free = result_from_dimension_range(
device, logger, config, model, 1, max_dim
)
else:
detailed_result, memory_used, memory_free = result_from_dimension_range(
device, logger, config, model, test_dim, test_dim
)
if test == False:
# get mean
# get std
mean_time, std_time = ut.get_mean_std(detailed_result)
mean_memory_used, std_memory_used = ut.get_mean_std(memory_used)
mean_memory_free, std_memory_free = ut.get_mean_std(memory_free)
# make folder for saving results
plt_title = "Model: {} | GPU: {} | Memory: {} MB".format(
model_name, device_name, total
)
date = "_".join(str(time.ctime()).split())
date = "_".join(date.split(":"))
foldername = date
os.mkdir("results/" + foldername)
# plot data
ut.plot_data(
foldername,
"dimension_vs_meantime",
mean_time,
"Dimensionn of Patch(nxn)",
"Mean Processing Time: LR -> SR, Scale: {} ( {} runs )".format(scale, run),
mode="mean time",
title=plt_title,
)
ut.plot_data(
foldername,
"dimension_vs_stdtime",
std_time,
"Dimension n of Patch(nxn)",
"Std of Processing Time: LR -> SR, Scale: {} ( {} runs )".format(
scale, run
),
mode="std time",
title=plt_title,
)
ut.plot_data(
foldername,
"dimension_vs_meanmemoryused",
mean_memory_used,
"Dimension n of Patch(nxn)",
"Mean Memory used: LR -> SR, Scale: {} ( {} runs )".format(scale, run),
mode="mean memory used",
title=plt_title,
)
ut.plot_data(
foldername,
"dimension_vs_stdmemoryused",
std_memory_used,
"Dimension n of Patch(nxn)",
"Std Memory Used: LR -> SR, Scale: {} ( {} runs )".format(scale, run),
mode="std memory used",
title=plt_title,
)
ut.plot_data(
foldername,
"dimension_vs_meanmemoryfree",
mean_memory_free,
"Dimension n of Patch(nxn)",
"Mean Memory Free: LR -> SR, Scale: {} ( {} runs )".format(scale, run),
mode="mean memory free",
title=plt_title,
)
ut.plot_data(
foldername,
"dimension_vs_stdmemoryfree",
std_memory_free,
"Dimension n of Patch(nxn)",
"Std Memory Free: LR -> SR, Scale: {} ( {} runs )".format(scale, run),
mode="std memory free",
title=plt_title,
)
# save data
ut.save_csv(
foldername,
"total_stat",
device,
device_name,
total,
mean_time,
std_time,
mean_memory_used,
std_memory_used,
mean_memory_free,
std_memory_free,
)
def result_from_dimension_range(device, logger, config, model, first, last):
"""
Get detailed result for every dimension from 1 to the last acceptable dimension
Parameters
----------
device : str
device type.
model : torch.nn.model
SR model.
first : int
starting dimension.
last : int
last acceptable dimension.
run : int, optional
total run to average the result. The default is 10.
Returns
-------
result3 : dictionary
time for every dimension.
memory_used : dictionary
memory used per dimension.
memory_free : dictionary
memory free per dimension.
"""
run = config["run"]
print("\nPreparing detailed data... ")
result3 = {}
memory_used = {}
memory_free = {}
for i in range(run):
print("\nRun: ", i + 1)
print()
for dim in tqdm(range(first, last + 1)):
dimension = dim
input_image = ut.random_image(dimension)
input_image = input_image.to(device)
with torch.no_grad():
try:
print("\n")
print(input_image.shape)
print(input_image[0, 0, 0, 0:5])
start = time.time()
output_image = model(input_image)
end = time.time()
total_time = end - start
print("Processing time: ", total_time)
print("\n")
if dimension in result3.keys():
result3[dimension].append(total_time)
_, used, free = ut.get_gpu_details(
device, "", None, print_details=False
)
memory_used[dimension].append(used)
memory_free[dimension].append(free)
else:
result3[dimension] = [total_time]
_, used, free = ut.get_gpu_details(
device, "", None, print_details=False
)
memory_used[dimension] = [used]
memory_free[dimension] = [free]
ut.clear_cuda(input_image, output_image)
except RuntimeError as err:
logger.exception("\nDimension NOT OK!")
state = "\nGPU usage after dimension exception...\n"
ut.get_gpu_details(device, state, logger, print_details=True)
output_image = None
ut.clear_cuda(input_image, output_image)
state = f"\nGPU usage after clearing the image {dimension}x{dimension}...\n"
ut.get_gpu_details(device, state, logger, print_details=True)
break
ut.clear_cuda(None, None)
subprocess.run("gpustat", shell=True)
return result3, memory_used, memory_free
def forward_chop_iterative(x,
model=None,
shave=10,
min_size=1024,
device="cuda",
print_result=True):
dim = int(math.sqrt(min_size)) # getting patch dimension
b, c, h, w = x.size() # current image batch, channel, height, width
device = device
patch_count = 0
output = torch.tensor(np.zeros((b, c, h * 4, w * 4)))
total_time = 0
total_crop_time = 0
total_shift_time = 0
total_clear_time = 0
if device == "cuda":
x = x.to(device)
new_i_s = 0
for i in range(0, h, dim - 2 * shave):
new_j_s = 0
new_j_e = 0
for j in range(0, w, dim - 2 * shave):
patch_count += 1
h_s, h_e = i, min(h, i + dim) # patch height start and end
w_s, w_e = j, min(w, j + dim) # patch width start and end
lr = x[:, :, h_s:h_e, w_s:w_e]
with torch.no_grad():
# EDSR processing
start = time.time()
sr = model(lr)
end = time.time()
processing_time = end - start
total_time += processing_time
# new cropped patch's dimension (h and w)
n_h_s, n_h_e, n_w_s, n_w_e = 0, 0, 0, 0
n_h_s = 0 if h_s == 0 else (shave * 4)
n_h_e = ((h_e - h_s) * 4) if h_e == h else (((h_e - h_s) - shave) *
4)
new_i_e = new_i_s + n_h_e - n_h_s
n_w_s = 0 if w_s == 0 else (shave * 4)
n_w_e = ((w_e - w_s) * 4) if w_e == w else (((w_e - w_s) - shave) *
4)
new_j_e = new_j_e + n_w_e - n_w_s
# corpping image in
crop_start = time.time()
sr_small = sr[:, :, n_h_s:n_h_e, n_w_s:n_w_e]
crop_end = time.time()
crop_time = crop_end - crop_start
total_crop_time += crop_time
# =============================================================================
# print('Crop time: ', crop_time)
# =============================================================================
shift_start = time.time()
if device == "cuda":
sr_small = sr_small.to("cpu")
shift_end = time.time()
shift_time = shift_end - shift_start
total_shift_time += shift_time
# =============================================================================
# print('Shift time: ', shift_time)
# =============================================================================
output[:, :, new_i_s:new_i_e, new_j_s:new_j_e] = sr_small
del sr_small
if w_e == w:
break
new_j_s = new_j_e
clear_start = time.time()
if device == "cuda":
ut.clear_cuda(lr, sr)
clear_end = time.time()
clear_time = clear_end - clear_start
total_clear_time += clear_time
new_i_s = new_i_e
if h_e == h:
break
if print_result == True:
print("Patch dimension: {}x{}".format(dim, dim))
print("Total pacthes: ", patch_count)
print("Total EDSR Processing time: ", total_time)
print("Total crop time: ", total_crop_time)
print("Total shift time: ", total_shift_time)
print("Total clear time: ", total_clear_time)
return output, total_time, total_crop_time, total_shift_time, total_clear_time